UNIVERSIDAD PABLO DE OLAVIDE
Publications
2025
A.R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
Feature Importance in Association Rule-Based Explanations for Time Series Forecasting Conference
IDEAL 26th International Conference on Intelligent Data Engineering and Automated Learning, Lecture Notes in Artificial Intelligence 2025.
Links | BibTeX | Tags: association rules, forecasting, time series, XAI
@conference{IDEAL2025_Angela,
title = {Feature Importance in Association Rule-Based Explanations for Time Series Forecasting},
author = {A.R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/chapter/10.1007/978-3-032-10489-2_20},
doi = {10.1007/978-3-032-10489-2_20},
year = {2025},
date = {2025-11-13},
urldate = {2025-11-13},
booktitle = {IDEAL 26th International Conference on Intelligent Data Engineering and Automated Learning},
series = {Lecture Notes in Artificial Intelligence},
keywords = {association rules, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
Z. Wang and I. Koprinska and M. Martínez-Ballesteros and A. Troncoso and B. Jeffries
AIED 26th International Conference on Artificial Intelligence in Education, 2025.
Links | BibTeX | Tags: association rules, education
@conference{AIED2025,
title = {Comparison of Explainable Machine Learning Methods for Early Prediction of Student Performance in Programming Courses},
author = {Z. Wang and I. Koprinska and M. Martínez-Ballesteros and A. Troncoso and B. Jeffries },
url = {https://link.springer.com/chapter/10.1007/978-3-031-99264-3_20},
doi = {https://doi.org/10.1007/978-3-031-99264-3_20},
year = {2025},
date = {2025-07-24},
urldate = {2025-07-24},
booktitle = {AIED 26th International Conference on Artificial Intelligence in Education},
keywords = {association rules, education},
pubstate = {published},
tppubtype = {conference}
}
C. Herruzo-Lodeiro and F. Rodríguez-Díaz and A. Troncoso and M. Martínez-Ballesteros
SAC 40th ACM/SIGAPP Symposium on Applied Computing, 2025.
Links | BibTeX | Tags: association rules, pattern recognition
@conference{SAC2025,
title = {Bioinspired evolutionary metaheuristic based on COVID spread for discovering numerical association rules},
author = {C. Herruzo-Lodeiro and F. Rodríguez-Díaz and A. Troncoso and M. Martínez-Ballesteros},
doi = {10.1145/3672608.3707787},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
booktitle = {SAC 40th ACM/SIGAPP Symposium on Applied Computing},
pages = {138-144},
keywords = {association rules, pattern recognition},
pubstate = {published},
tppubtype = {conference}
}
N. Ullah and F. Guzmán-Aroca and F. Martínez-Álvarez and I. De Falco and G. Sannino
A Novel Explainable AI Framework for Medical Image Classification Integrating Statistical, Visual, and Rule-Based Methods Journal Article
In: Medical Image Analysis, vol. 105, pp. 103665, 2025.
Abstract | Links | BibTeX | Tags: association rules, deep learning, feature selection, XAI
@article{ULLAH25,
title = {A Novel Explainable AI Framework for Medical Image Classification Integrating Statistical, Visual, and Rule-Based Methods},
author = {N. Ullah and F. Guzmán-Aroca and F. Martínez-Álvarez and I. De Falco and G. Sannino},
url = {https://www.sciencedirect.com/science/article/pii/S1361841525002129},
doi = {https://doi.org/10.1016/j.media.2025.103665},
year = {2025},
date = {2025-06-06},
urldate = {2025-06-06},
journal = {Medical Image Analysis},
volume = {105},
pages = {103665},
abstract = {Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in highstakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability’s depth and clarity. This work proposes a novel explainable AI method specifically designed for medical image analysis, integrating statistical, visual, and rule-based explanations to improve transparency in deep learning models. Statistical features are derived from deep features extracted using a custom Mobilenetv2 model. A two-step feature selection method—zero-based filtering with mutual importance selection—ranks and refines these features. Decision tree and RuleFit models
are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets—COVID-19 radiography, ultrasound
breast cancer, brain tumour magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images—with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.},
keywords = {association rules, deep learning, feature selection, XAI},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in highstakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability’s depth and clarity. This work proposes a novel explainable AI method specifically designed for medical image analysis, integrating statistical, visual, and rule-based explanations to improve transparency in deep learning models. Statistical features are derived from deep features extracted using a custom Mobilenetv2 model. A two-step feature selection method—zero-based filtering with mutual importance selection—ranks and refines these features. Decision tree and RuleFit models
are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets—COVID-19 radiography, ultrasound
breast cancer, brain tumour magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images—with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.
are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets—COVID-19 radiography, ultrasound
breast cancer, brain tumour magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images—with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
A new metric based on association rules to assess explainability techniques for time series forecasting Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 5, pp. 4140-4155, 2025.
Abstract | Links | BibTeX | Tags: association rules, forecasting, time series, XAI
@article{TRONCOSO-GARCIA25,
title = {A new metric based on association rules to assess explainability techniques for time series forecasting},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://ieeexplore.ieee.org/document/10879535},
doi = {10.1109/TPAMI.2025.3540513},
year = {2025},
date = {2025-02-11},
urldate = {2025-02-11},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {47},
number = {5},
pages = {4140-4155},
abstract = {This paper introduces a new, model-independent, metric, called RExQUAL, for quantifying the quality of explanations provided by attribution-based explainable artificial intelligence techniques and compare them. The underlying idea is based on feature attribution, using a subset of the ranking of the attributes highlighted by a model-agnostic explainable method in a forecasting task. Then, association rules are generated using these key attributes as input data. Novel metrics, including global support and confidence, are proposed to assess the joint quality of generated rules. Finally, the quality of the explanations is calculated based on a wise and comprehensive combination of the association rules global metrics. The proposed method integrates local explanations through attribution-based approaches for evaluation and feature selection with global explanations for the entire dataset. This paper rigorously evaluates the new metric by comparing three explainability techniques: the widely used SHAP and LIME, and the novel methodology RULEx. The experimental design includes predicting time series of different natures, including univariate and multivariate, through deep learning models. The results underscore the efficacy and versatility of the proposed methodology as a quantitative framework for evaluating and comparing explainable techniques.},
keywords = {association rules, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a new, model-independent, metric, called RExQUAL, for quantifying the quality of explanations provided by attribution-based explainable artificial intelligence techniques and compare them. The underlying idea is based on feature attribution, using a subset of the ranking of the attributes highlighted by a model-agnostic explainable method in a forecasting task. Then, association rules are generated using these key attributes as input data. Novel metrics, including global support and confidence, are proposed to assess the joint quality of generated rules. Finally, the quality of the explanations is calculated based on a wise and comprehensive combination of the association rules global metrics. The proposed method integrates local explanations through attribution-based approaches for evaluation and feature selection with global explanations for the entire dataset. This paper rigorously evaluates the new metric by comparing three explainability techniques: the widely used SHAP and LIME, and the novel methodology RULEx. The experimental design includes predicting time series of different natures, including univariate and multivariate, through deep learning models. The results underscore the efficacy and versatility of the proposed methodology as a quantitative framework for evaluating and comparing explainable techniques.
2023
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso
A new approach based on association rules to add explainability to time series forecasting models Journal Article
In: Information Fusion, vol. 94, pp. 169-180, 2023.
Abstract | Links | BibTeX | Tags: association rules, time series, XAI
@article{TRONCOSO-GARCIA23,
title = {A new approach based on association rules to add explainability to time series forecasting models},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295},
doi = {10.1016/j.inffus.2023.01.021},
year = {2023},
date = {2023-01-22},
journal = {Information Fusion},
volume = {94},
pages = {169-180},
abstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.},
keywords = {association rules, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.
2022
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
Explainable machine learning for sleep apnea prediction Conference
KES International Conference on Knowledge Based and Intelligent information and Engineering Systems, 2022.
Abstract | Links | BibTeX | Tags: association rules, deep learning, time series, XAI
@conference{TRONCOSO-GARCIA22,
title = {Explainable machine learning for sleep apnea prediction},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922012406},
doi = {https://doi.org/10.1016/j.procs.2022.09.351},
year = {2022},
date = {2022-09-10},
booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems},
pages = {2930-2939},
abstract = {Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.},
keywords = {association rules, deep learning, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.
C. Segarra-Martín and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez
A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm Conference
SAC 37th Symposium On Applied Computing, 2022.
Abstract | BibTeX | Tags: association rules
@conference{SAC2022,
title = {A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm },
author = {C. Segarra-Martín and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},
year = {2022},
date = {2022-01-01},
booktitle = {SAC 37th Symposium On Applied Computing},
abstract = {The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. In addition, the CVOA setting parameters have been updated and a vaccination rate based on real data has been incorporated, to make it more efficient. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes.},
keywords = {association rules},
pubstate = {published},
tppubtype = {conference}
}
The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. In addition, the CVOA setting parameters have been updated and a vaccination rate based on real data has been incorporated, to make it more efficient. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes.
2020
F. Moleshi and A. Haeri and F. Martínez-Álvarez
A novel hybrid GA–PSO framework for mining quantitative association rules Journal Article
In: Soft Computing, vol. 24, no. 6, pp. 4645-4666, 2020.
Abstract | Links | BibTeX | Tags: association rules
@article{MOLESHI20,
title = {A novel hybrid GA–PSO framework for mining quantitative association rules},
author = {F. Moleshi and A. Haeri and F. Martínez-Álvarez},
url = {https://link.springer.com/article/10.1007/s00500-019-04226-6},
doi = {https://doi.org/10.1007/s00500-019-04226-6},
year = {2020},
date = {2020-03-01},
journal = {Soft Computing},
volume = {24},
number = {6},
pages = {4645-4666},
abstract = {Discovering association rules is a useful and common technique for data mining in which dependencies among datasets are shown. Discovering the rules from continuous numeric datasets is one of the common challenges in data mining. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the criteria of support and confidence. By drawing on two heuristic optimization techniques, to wit, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, a hybrid algorithm for extracting quantitative association rules was developed in this research. Accurate and interpretable rules result from the integration of the multiple objectives GA with the multiple objective PSO algorithms, which redresses the balance in the exploitation and exploration tasks. The useful and appropriate rules and the most suitable numerical intervals are discovered by proposing a multi-criteria method in which there is no need to discretize numerical values and to determine threshold values of minimum support and confidence. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness and comprehensibility. The results gained over five real-world datasets evidence the effectiveness of the proposed method. By hybridization of the GA and the PSO algorithm, the proposed approach has achieved considerable improvements compared with the basic algorithms in the criteria of the number of extracted rules from dataset, high confidence measure and support percentage.},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
Discovering association rules is a useful and common technique for data mining in which dependencies among datasets are shown. Discovering the rules from continuous numeric datasets is one of the common challenges in data mining. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the criteria of support and confidence. By drawing on two heuristic optimization techniques, to wit, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, a hybrid algorithm for extracting quantitative association rules was developed in this research. Accurate and interpretable rules result from the integration of the multiple objectives GA with the multiple objective PSO algorithms, which redresses the balance in the exploitation and exploration tasks. The useful and appropriate rules and the most suitable numerical intervals are discovered by proposing a multi-criteria method in which there is no need to discretize numerical values and to determine threshold values of minimum support and confidence. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness and comprehensibility. The results gained over five real-world datasets evidence the effectiveness of the proposed method. By hybridization of the GA and the PSO algorithm, the proposed approach has achieved considerable improvements compared with the basic algorithms in the criteria of the number of extracted rules from dataset, high confidence measure and support percentage.
2016
M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme
Improving a multi-objective evolutionary algorithm to discover quantitative association rules Journal Article
In: Knowledge and Information Systems, vol. 49, pp. 481-509, 2016.
Links | BibTeX | Tags: association rules
@article{MARTINEZ16,
title = {Improving a multi-objective evolutionary algorithm to discover quantitative association rules},
author = {M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme},
url = {https://link.springer.com/article/10.1007/s10115-015-0911-y},
doi = {https://doi.org/10.1007/s10115-015-0911-y},
year = {2016},
date = {2016-11-01},
journal = {Knowledge and Information Systems},
volume = {49},
pages = {481-509},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme
Obtaining optimal quality measures for quantitative association rules Journal Article
In: Neurocomputing, vol. 176, pp. 36-47, 2016.
Abstract | Links | BibTeX | Tags: association rules
@article{NEUCOM2016,
title = {Obtaining optimal quality measures for quantitative association rules},
author = {M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},
url = {https://www.sciencedirect.com/science/article/pii/S0925231215005688},
doi = {10.1016/j.neucom.2014.10.100},
year = {2016},
date = {2016-01-01},
journal = {Neurocomputing},
volume = {176},
pages = {36-47},
abstract = {There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user׳s decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending on the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real-world applications related to ozone and earthquakes are reported.},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user׳s decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending on the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real-world applications related to ozone and earthquakes are reported.
2015
M. Martínez-Ballesteros and J. Bacardit and A. Troncoso and J. C. Riquelme
Mining Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets Journal Article
In: Integrated Computer-Aided Engineering, vol. 22, no. 1, pp. 21-39, 2015.
Abstract | Links | BibTeX | Tags: association rules
@article{ICAE2015,
title = {Mining Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets},
author = {M. Martínez-Ballesteros and J. Bacardit and A. Troncoso and J. C. Riquelme},
url = {https://content.iospress.com/articles/integrated-computer-aided-engineering/ica00479},
doi = {10.3233/ICA-140479},
year = {2015},
date = {2015-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {22},
number = {1},
pages = {21-39},
abstract = {Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find interesting association rules. However, the rule-matching task of such techniques usually requires high computational and memory requirements. The use of efficient computational techniques has become a task of the utmost importance due to the high volume of generated data nowadays. Hence, this paper aims at improving the scalability of quantitative association rule mining techniques based on genetic algorithms to handle large-scale datasets without quality loss in the results obtained. For this purpose, a new representation of the individuals, new genetic operators and a windowing-based learning scheme are proposed to achieve successfully such challenging task. Specifically, the proposed techniques are integrated into the multi-objective evolutionary algorithm named QARGA-M to assess their performances. Both the standard version and the enhanced one of QARGA-M have been tested in several datasets that present different number of attributes and instances. Furthermore, the proposed methodologies have been integrated into other existing techniques based in genetic algorithms to discover quantitative association rules. The comparative analysis performed shows significant improvements of QARGA-M and other existing genetic algorithms in terms of computational costs without losing quality in the results when the proposed techniques are applied.},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find interesting association rules. However, the rule-matching task of such techniques usually requires high computational and memory requirements. The use of efficient computational techniques has become a task of the utmost importance due to the high volume of generated data nowadays. Hence, this paper aims at improving the scalability of quantitative association rule mining techniques based on genetic algorithms to handle large-scale datasets without quality loss in the results obtained. For this purpose, a new representation of the individuals, new genetic operators and a windowing-based learning scheme are proposed to achieve successfully such challenging task. Specifically, the proposed techniques are integrated into the multi-objective evolutionary algorithm named QARGA-M to assess their performances. Both the standard version and the enhanced one of QARGA-M have been tested in several datasets that present different number of attributes and instances. Furthermore, the proposed methodologies have been integrated into other existing techniques based in genetic algorithms to discover quantitative association rules. The comparative analysis performed shows significant improvements of QARGA-M and other existing genetic algorithms in terms of computational costs without losing quality in the results when the proposed techniques are applied.
2014
M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme
Selecting the Best Measures to Discover Quantitative Association Rules Journal Article
In: Neurocomputing, vol. 126, pp. 3-14, 2014.
Abstract | Links | BibTeX | Tags: association rules
@article{NEUCOM2014,
title = {Selecting the Best Measures to Discover Quantitative Association Rules},
author = {M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},
url = {https://www.sciencedirect.com/science/article/pii/S0925231213007029},
doi = {10.1016/j.neucom.2013.01.056},
year = {2014},
date = {2014-01-01},
journal = {Neurocomputing},
volume = {126},
pages = {3-14},
abstract = {The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better results are obtained when using the function fitness composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA with the original fitness function is provided, showing a remarkable improvement when the new one is used.},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better results are obtained when using the function fitness composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA with the original fitness function is provided, showing a remarkable improvement when the new one is used.
2013
M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme
A sensitivity analysis for quality measures of quantitative association rules Conference
HAIS 8th International Conference on Hibryd Artificial Intelligence Systems, Lecture Notes in Computer Science 2013.
Links | BibTeX | Tags: association rules
@conference{HAIS2013,
title = {A sensitivity analysis for quality measures of quantitative association rules},
author = {M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},
url = {https://link.springer.com/chapter/10.1007/978-3-642-40846-5_58},
year = {2013},
date = {2013-01-01},
booktitle = {HAIS 8th International Conference on Hibryd Artificial Intelligence Systems},
series = {Lecture Notes in Computer Science},
keywords = {association rules},
pubstate = {published},
tppubtype = {conference}
}
2011
M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme
An Evolutionary Algorithm to Discover Quantitative Association Rules in Multidimensional Time Series Journal Article
In: Soft Computing, vol. 15, no. 10, pp. 2065-2084, 2011.
Abstract | Links | BibTeX | Tags: association rules
@article{SOFTCO2011,
title = {An Evolutionary Algorithm to Discover Quantitative Association Rules in Multidimensional Time Series},
author = {M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},
url = {https://link.springer.com/article/10.1007/s00500-011-0705-4},
doi = {10.1007/s00500-011-0705-4},
year = {2011},
date = {2011-01-01},
journal = {Soft Computing},
volume = {15},
number = {10},
pages = {2065-2084},
abstract = {An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA.},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA.
M. Martínez-Ballesteros and C. Rubio-Escudero and J. C. Riquelme and F. Martínez-Álvarez
Mining quantitative association rules in microarray data using evolutive algorithms Conference
International Conference on Agents and Artificial Intelligence (ICAART'11), 2011.
BibTeX | Tags: association rules
@conference{ballesteros2011,
title = {Mining quantitative association rules in microarray data using evolutive algorithms},
author = {M. Martínez-Ballesteros and C. Rubio-Escudero and J. C. Riquelme and F. Martínez-Álvarez},
year = {2011},
date = {2011-01-01},
booktitle = {International Conference on Agents and Artificial Intelligence (ICAART'11)},
keywords = {association rules},
pubstate = {published},
tppubtype = {conference}
}
2010
M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme
Mining Quantitative Association Rules Based on Evolutionary Computation and its Application to Atmospheric Pollution Journal Article
In: Integrated Computer-Aided Engineering, vol. 17, pp. 227-242, 2010.
Abstract | Links | BibTeX | Tags: association rules
@article{ICAE2010,
title = {Mining Quantitative Association Rules Based on Evolutionary Computation and its Application to Atmospheric Pollution},
author = {M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme},
url = {https://content.iospress.com/articles/integrated-computer-aided-engineering/ica00340},
doi = {10.3233/ICA-2010-0340},
year = {2010},
date = {2010-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {17},
pages = {227-242},
abstract = {This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.},
keywords = {association rules},
pubstate = {published},
tppubtype = {article}
}
This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.
2009
M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme
Descubriendo Reglas de Asociación Numéricas entre Series Temporales Workshop
CAEPIA XIII Conferencia de la Asociación Española para la Inteligencia Artificial. MINCODA I Workshop International on Mining of Non-Conventional Data, 2009.
BibTeX | Tags: association rules
@workshop{MINCODA2009b,
title = {Descubriendo Reglas de Asociación Numéricas entre Series Temporales},
author = {M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},
year = {2009},
date = {2009-01-01},
booktitle = {CAEPIA XIII Conferencia de la Asociación Española para la Inteligencia Artificial.
MINCODA I Workshop International on Mining of Non-Conventional Data},
keywords = {association rules},
pubstate = {published},
tppubtype = {workshop}
}
M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme
Quantitative Association Rules Applied to Climatological Time Series Forecasting Conference
IDEAL Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science 2009.
Links | BibTeX | Tags: association rules
@conference{IDEAL2009,
title = {Quantitative Association Rules Applied to Climatological Time Series Forecasting},
author = {M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},
url = {https://link.springer.com/chapter/10.1007/978-3-642-04394-9_35},
year = {2009},
date = {2009-01-01},
booktitle = {IDEAL Intelligent Data Engineering and Automated Learning},
pages = {284-291},
series = {Lecture Notes in Computer Science},
keywords = {association rules},
pubstate = {published},
tppubtype = {conference}
}