UNIVERSIDAD PABLO DE OLAVIDE
Publications
2025
A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés and A. Troncoso
Improving monsoon forecasting based on feature selection and explainable artificial intelligence Journal Article
In: Applied Soft Computing, vol. 185, pp. 114053, 2025.
Links | BibTeX | Tags: feature selection, natural disasters, XAI
@article{ASOC2024,
title = {Improving monsoon forecasting based on feature selection and explainable artificial intelligence},
author = {A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S1568494625013663},
doi = {10.1016/j.asoc.2025.114053},
year = {2025},
date = {2025-12-02},
urldate = {2025-12-02},
journal = {Applied Soft Computing},
volume = {185},
pages = {114053},
keywords = {feature selection, natural disasters, XAI},
pubstate = {published},
tppubtype = {article}
}
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}
}
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.
A. M. Chacón-Maldonado and L. Melgar-García and G. Asencio-Cortés and A. Troncoso
A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting Journal Article
In: Neural Computing and Applications, vol. 37, pp. 3245-3264, 2025.
Links | BibTeX | Tags: deep learning, precision agriculture, XAI
@article{Chacon2025,
title = {A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting},
author = {A. M. Chacón-Maldonado and L. Melgar-García and G. Asencio-Cortés and A. Troncoso},
url = {https://link.springer.com/article/10.1007/s00521-024-10731-z},
doi = {https://doi.org/10.1007/s00521-024-10731-z},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Neural Computing and Applications},
volume = {37},
pages = {3245-3264},
keywords = {deep learning, precision agriculture, XAI},
pubstate = {published},
tppubtype = {article}
}
2024
M. J. Jiménez-Navarro and A. R. Troncoso-García and A. Troncoso and F. Martínez-Álvarez and M. Martínez-Ballesteros
Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting Conference
SST International Conference on Smart Systems and Technologies, 2024.
Abstract | Links | BibTeX | Tags: deep learning, energy, feature selection, XAI
@conference{SST2024,
title = {Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting},
author = {M. J. Jiménez-Navarro and A. R. Troncoso-García and A. Troncoso and F. Martínez-Álvarez and M. Martínez-Ballesteros},
url = {https://ieeexplore.ieee.org/document/10755283},
doi = {10.1109/SST61991.2024.10755283},
year = {2024},
date = {2024-10-16},
urldate = {2024-10-16},
booktitle = {SST International Conference on Smart Systems and Technologies},
pages = {153-158},
abstract = {Electricity demand forecasting is an important part of the energy industry strategy. Accurate predictions are crucial for maintaining a stable energy supply, planning production, managing distribution, preventing grid overloads, integrating renewable energy sources, and reducing costs and environmental impact. Machine learning and, in particular, deep learning are promising techniques to improve the prediction accuracy of electric demand, but face challenges related to a lack of interpretability due to the “black box” nature of some models. Feature selection methods address these issues by identifying relevant features and simplifying the learning process. This paper aims to explain the most critical lags that impact electric demand forecasting in Spain using the temporal selection layer technique within deep learning models for time series forecasting. This technique transforms a neural network into a model with embedded feature selection, aiming to enhance efficacy and interpretability while reducing computational costs. The results were compared with other methods that incorporate an embedded feature selection mechanism to select the best model. Furthermore, an explainable technique is used to assess the feature importance in the best model over the last year to understand how input features influence electric demand forecasting and provide insights into their contributions and interactions. The results show that our approach improves both the efficacy and interpretability in the context of electric demand forecasting.},
keywords = {deep learning, energy, feature selection, XAI},
pubstate = {published},
tppubtype = {conference}
}
Electricity demand forecasting is an important part of the energy industry strategy. Accurate predictions are crucial for maintaining a stable energy supply, planning production, managing distribution, preventing grid overloads, integrating renewable energy sources, and reducing costs and environmental impact. Machine learning and, in particular, deep learning are promising techniques to improve the prediction accuracy of electric demand, but face challenges related to a lack of interpretability due to the “black box” nature of some models. Feature selection methods address these issues by identifying relevant features and simplifying the learning process. This paper aims to explain the most critical lags that impact electric demand forecasting in Spain using the temporal selection layer technique within deep learning models for time series forecasting. This technique transforms a neural network into a model with embedded feature selection, aiming to enhance efficacy and interpretability while reducing computational costs. The results were compared with other methods that incorporate an embedded feature selection mechanism to select the best model. Furthermore, an explainable technique is used to assess the feature importance in the best model over the last year to understand how input features influence electric demand forecasting and provide insights into their contributions and interactions. The results show that our approach improves both the efficacy and interpretability in the context of electric demand forecasting.
A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros
Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection Conference
SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications, Lecture Notes in Networks and Systems 2024.
Links | BibTeX | Tags: deep learning, forecasting, time series, XAI
@conference{SOCO24_Troncoso,
title = {Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection},
author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros},
url = {https://link.springer.com/chapter/10.1007/978-3-031-75013-7_14},
doi = {https://doi.org/10.1007/978-3-031-75013-7_14},
year = {2024},
date = {2024-10-10},
booktitle = {SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications},
pages = {139-149},
series = {Lecture Notes in Networks and Systems},
keywords = {deep learning, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
F. Rodríguez-Díaz and A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés
Explainable Olive grove and Grapevine pest forecasting through machine learning-based classification and regression Journal Article
In: Results in Engineering, vol. 24, pp. 103058, 2024.
Abstract | Links | BibTeX | Tags: deep learning, feature selection, time series, XAI
@article{RODRIGUEZ24,
title = {Explainable Olive grove and Grapevine pest forecasting through machine learning-based classification and regression},
author = {F. Rodríguez-Díaz and A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés},
url = {https://www.sciencedirect.com/science/article/pii/S2590123024013136},
doi = {https://doi.org/10.1016/j.rineng.2024.103058},
year = {2024},
date = {2024-09-09},
urldate = {2024-09-09},
journal = {Results in Engineering},
volume = {24},
pages = {103058},
abstract = {Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.},
keywords = {deep learning, feature selection, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.
A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso
Ground-Level Ozone Forecasting using Explainable Machine Learning Conference
CAEPIA Conference of the Spanish Association for Artificial Intelligence, vol. 14640, Lecture Notes in Artificial Intelligence 2024.
Abstract | Links | BibTeX | Tags: forecasting, time series, XAI
@conference{TRONCOSO-GARCIA24c,
title = {Ground-Level Ozone Forecasting using Explainable Machine Learning},
author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_8},
doi = {https://doi.org/10.1007/978-3-031-62799-6_8},
year = {2024},
date = {2024-06-06},
booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence},
volume = {14640},
pages = {71-80},
series = {Lecture Notes in Artificial Intelligence},
abstract = {The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.},
keywords = {forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés
Explaining deep learning models for ozone pollution prediction via embedded feature selection Journal Article
In: Applied Soft Computing, vol. 157, pp. 111504, 2024.
Abstract | Links | BibTeX | Tags: deep learning, feature selection, time series, XAI
@article{JIMENEZ-NAVARRO24,
title = {Explaining deep learning models for ozone pollution prediction via embedded feature selection},
author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},
url = {https://www.sciencedirect.com/science/article/pii/S1568494624002783},
doi = {https://doi.org/10.1016/j.asoc.2024.111504},
year = {2024},
date = {2024-04-04},
journal = {Applied Soft Computing},
volume = {157},
pages = {111504},
abstract = {Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects climate warming, crop productivity, and more. Its formation occurs when nitrogen oxides and volatile organic compounds react with short-wavelength solar radiation. Consequently, urban areas with high traffic volume and elevated temperatures are particularly prone to elevated O3 levels, which pose a significant health risk to their inhabitants. In response to this problem, many countries have developed web and mobile applications that provide real-time air pollution information using sensor data. However, while these applications offer valuable insight into current pollution levels, predicting future pollutant behavior is crucial for effective planning and mitigation strategies. Therefore, our main objectives are to develop accurate and efficient prediction models and identify the key factors that influence O3 levels. We adopt a time series forecasting approach to address these objectives, which allows us to analyze and predict O3 future behavior. Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability. Our study focuses on data collected from five representative areas in Seville, Cordova, and Jaen provinces in Spain, using multiple sensors to capture comprehensive pollution data. We compare the performance of three models: Lasso, Decision Tree, and Deep Learning with and without incorporating the Time Selection Layer. Our results demonstrate that including the Time Selection Layer significantly enhances the effectiveness and interpretability of Deep Learning models, achieving an average effectiveness improvement of 9% across all monitored areas.},
keywords = {deep learning, feature selection, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects climate warming, crop productivity, and more. Its formation occurs when nitrogen oxides and volatile organic compounds react with short-wavelength solar radiation. Consequently, urban areas with high traffic volume and elevated temperatures are particularly prone to elevated O3 levels, which pose a significant health risk to their inhabitants. In response to this problem, many countries have developed web and mobile applications that provide real-time air pollution information using sensor data. However, while these applications offer valuable insight into current pollution levels, predicting future pollutant behavior is crucial for effective planning and mitigation strategies. Therefore, our main objectives are to develop accurate and efficient prediction models and identify the key factors that influence O3 levels. We adopt a time series forecasting approach to address these objectives, which allows us to analyze and predict O3 future behavior. Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability. Our study focuses on data collected from five representative areas in Seville, Cordova, and Jaen provinces in Spain, using multiple sensors to capture comprehensive pollution data. We compare the performance of three models: Lasso, Decision Tree, and Deep Learning with and without incorporating the Time Selection Layer. Our results demonstrate that including the Time Selection Layer significantly enhances the effectiveness and interpretability of Deep Learning models, achieving an average effectiveness improvement of 9% across all monitored areas.
L. Melgar-García and A. Troncoso
A novel incremental ensemble learning for real-time explainable forecasting of electricity price Journal Article
In: Knowledge-Based Systems, vol. 305, pp. 112574, 2024.
Links | BibTeX | Tags: energy, IoT, time series, XAI
@article{Melgar2024,
title = {A novel incremental ensemble learning for real-time explainable forecasting of electricity price},
author = {L. Melgar-García and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124012085},
doi = {https://doi.org/10.1016/j.knosys.2024.112574},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Knowledge-Based Systems},
volume = {305},
pages = {112574},
keywords = {energy, IoT, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
2023
A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez
Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting Journal Article
In: Computers and Electronics in Agriculture, vol. 215, pp. 108387, 2023.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, precision agriculture, XAI
@article{TRONCOSO-GARCIA23b,
title = {Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting},
author = {A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S0168169923007755},
doi = {https://doi.org/10.1016/j.compag.2023.108387},
year = {2023},
date = {2023-11-08},
journal = {Computers and Electronics in Agriculture},
volume = {215},
pages = {108387},
abstract = {Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time},
keywords = {deep learning, forecasting, precision agriculture, XAI},
pubstate = {published},
tppubtype = {article}
}
Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time
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.
A. R. Troncoso-García and m. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
Evolutionary computation to explain deep learning models for time series forecasting Conference
SAC 38th Annual ACM Symposium on Applied Computing, 2023.
Links | BibTeX | Tags: deep learning, time series, XAI
@conference{SAC2023,
title = {Evolutionary computation to explain deep learning models for time series forecasting},
author = {A. R. Troncoso-García and m. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://dl.acm.org/doi/abs/10.1145/3555776.3578994},
year = {2023},
date = {2023-01-01},
booktitle = {SAC 38th Annual ACM Symposium on Applied Computing},
keywords = {deep learning, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
L. Melgar-García and A. R. Troncoso-García and D. Gutiérrez-Avilés and J. F. Torres and A. Troncoso
Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python Conference
ICEUTE 14th International Conference on European Transnational Educational, Lecture Notes in Networks and Systems 2023.
@conference{ICEUTE23_Laura,
title = {Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python},
author = {L. Melgar-García and A. R. Troncoso-García and D. Gutiérrez-Avilés and J. F. Torres and A. Troncoso},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {ICEUTE 14th International Conference on European Transnational Educational},
series = {Lecture Notes in Networks and Systems},
keywords = {education, XAI},
pubstate = {published},
tppubtype = {conference}
}
A. M. Chacón-Maldonado and A. R. Troncoso-García and F. Martínez-Álvarez and G. Asencio-Cortés and A. Troncoso
Olive oil fly population pest forecasting using explainable deep learning Conference
SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications , Lecture Notes in Networks and Systems 2023.
BibTeX | Tags: precision agriculture, XAI
@conference{SOCO23_Andres,
title = {Olive oil fly population pest forecasting using explainable deep learning},
author = {A. M. Chacón-Maldonado and A. R. Troncoso-García and F. Martínez-Álvarez and G. Asencio-Cortés and A. Troncoso},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications },
series = {Lecture Notes in Networks and Systems},
keywords = {precision agriculture, XAI},
pubstate = {published},
tppubtype = {conference}
}
M. J. Jiménez-Navarro and M. Martínez-Ballesteros1 and F. Martínez-Álvarez and G. Asencio-Cortés
Explaining learned patterns in deep learning by association rules mining Conference
SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, Lecture Notes in Networks and Systems 2023.
Abstract | Links | BibTeX | Tags: association rules, deep learning, XAI
@conference{SOCO23_Manu,
title = {Explaining learned patterns in deep learning by association rules mining},
author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros1 and F. Martínez-Álvarez and G. Asencio-Cortés},
url = {https://link.springer.com/chapter/10.1007/978-3-031-42536-3_13},
doi = {https://doi.org/10.1007/978-3-031-42536-3_13},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications},
pages = {132-141},
series = {Lecture Notes in Networks and Systems},
abstract = {This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability
of prediction outcomes. The study aims to gain insights into the patterns that were learned correctly or incorrectly by the model. To identify these scenarios, an association rule algorithm is applied to extract the patterns learned by the deep learning model. The rules are then analyzed and classified based on specific metrics to draw conclusions about the behavior of the model. We applied this approach to a well-known dataset in various scenarios, such as underfitting and overfitting. The results demonstrate that the combination of the two techniques is highly effective in identifying the patterns learned by the model and analyzing its performance in different scenarios, through error analysis. We suggest that this methodology can enhance the transparency and
interpretability of black-box models, thus improving their reliability for real-world applications.},
keywords = {association rules, deep learning, XAI},
pubstate = {published},
tppubtype = {conference}
}
This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability
of prediction outcomes. The study aims to gain insights into the patterns that were learned correctly or incorrectly by the model. To identify these scenarios, an association rule algorithm is applied to extract the patterns learned by the deep learning model. The rules are then analyzed and classified based on specific metrics to draw conclusions about the behavior of the model. We applied this approach to a well-known dataset in various scenarios, such as underfitting and overfitting. The results demonstrate that the combination of the two techniques is highly effective in identifying the patterns learned by the model and analyzing its performance in different scenarios, through error analysis. We suggest that this methodology can enhance the transparency and
interpretability of black-box models, thus improving their reliability for real-world applications.
of prediction outcomes. The study aims to gain insights into the patterns that were learned correctly or incorrectly by the model. To identify these scenarios, an association rule algorithm is applied to extract the patterns learned by the deep learning model. The rules are then analyzed and classified based on specific metrics to draw conclusions about the behavior of the model. We applied this approach to a well-known dataset in various scenarios, such as underfitting and overfitting. The results demonstrate that the combination of the two techniques is highly effective in identifying the patterns learned by the model and analyzing its performance in different scenarios, through error analysis. We suggest that this methodology can enhance the transparency and
interpretability of black-box models, thus improving their reliability for real-world applications.
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.