Citations and socials
Federico Divina obtained his Ph.D. in Artificial Intelligence from the Vrije Universiteit of Amsterdam, and after that he worked as a postdoc at the University of Tilburg, within the European project NEWTIES. In 2006 he moved to the Pablo de Olavide University, where he is actually an Associate Professor.
He has been working on knowledge extraction since his Ph.D. thesis at the Vrije Universiteit of Amsterdam. He has extensive experience in the application of Machine Learning, especially techniques based on Soft Computing, for the extraction of knowledge from massive data.
His main research interests are:
- Bioinformatics
- Evolutionary Computation
- Machine Learning
- Big Data
Projects
Federico Divina has participated in various research project projects, for instance:
- Differential: this project aims to develop new tools and methods to manage and analyse information coming from several sources with the final goal of better understanding how and when energy is consumed in distributed facilities. This project was developed as a coordinated project with three complementary research groups from three different universities (Universidad de Granada, Universidad Pablo de Olavide and Universidad de Castilla La Mancha).
- GALICIAME: project that aimed at applying machine learning tools in order to extract knowledge from genetic data related to spinal muscular atrophy (SMA), in collaboration with the “Centro Andaluz de Biología del Desarrollo” (CABD).
- NEWTIES: EU project that aimed at developing an artificial society. This project involved the Vrije Universiteit van Amsterm, the University of Tilburg, the Napier University, University of Surrey, Napier University and Eötvös Loránd University.
Publications
For a complete list of my publications, please visit my Google Scholar Profile or my ORCID.
2025 |
D. Rodríguez-Baena and F. Gómez-Vela and A. Lopez-Fernandez and M. García-Torres and F. Divina BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering Journal Article In: Applied Intelligence, vol. 55, no. 12, pp. 830, 2025. @article{rodriguez2025binrec,Recommender Systems help users in making decision in different fields such as purchases or what movies to watch. User-Based Collaborative Filtering (UBCF) approach is one of the most commonly used techniques for developing these software tools. It is based on the idea that users who have previously shared similar tastes will almost certainly share similar tastes in the future. As a result, determining the nearest users to the one for whom recommendations are sought (active user) is critical. However, the massive growth of online commercial data has made this task especially difficult. As a result, Biclustering techniques have been used in recent years to perform a local search for the nearest users in subgroups of users with similar rating behaviour under a subgroup of items (biclusters), rather than searching the entire rating database. Nevertheless, due to the large size of these databases, the number of biclusters generated can be extremely high, making their processing very complex. In this paper we propose BinRec, a novel UBCF approach based on Biclustering. BinRec simplifies the search for neighbouring users by determining which ones are nearest to the active user based on the number of biclusters shared by the users. Experimental results show that BinRec outperforms other state-of-the-art recommender systems, with a remarkable improvement in environments with high data sparsity. The flexibility and scalability of the method position it as an efficient alternative for common collaborative filtering problems such as sparsity or cold-start. |
T. Vanhaeren and L. Cataneo and F. Divina and P. M. Martínez-García Enhancing R-loop prediction with high-throughput sequencing data Journal Article In: NAR Genomics and Bioinformatics, vol. 7, no. 2, pp. lqaf077, 2025. @article{vanhaeren2025enhancing,R-loops are three-stranded RNA and DNA hybrid structures that often occur in the genome and play important roles in a variety of cellular processes from bacteria to mammals. Sequencing methods profiling R-loops genome-wide have revealed that they can form co-transcriptionally at cell type specific genes and associate with specific chromatin states during cell differentiation and reprogramming. However, current computational methods for the prediction of R-loops rely solely on their DNA sequence properties, which precludes detection across cell types, tissues or developmental stages. Here, we conduct a machine learning approach that allows the prediction of mammalian cell type-specific R-loops using sequence information and high-throughput sequencing signals. Our predictive models are induced from human samples and achieve highly accurate predictions, with transcriptomics, DNA features, chromatin accessibility and the active gene body H3K36me3 epigenomic mark being the most informative datasets. We generate de novo virtual R-loop maps that show high concordance with experimental ones and capture cell type specificity. Our approach compares favorably to sequence-based methods and can be generalized to mouse datasets. Based on this, we generate virtual R-loop maps in 51 mammalian systems that are freely accessible to the scientific community. |
T. Vanhaeren and A. R. Troncoso-García and J. F. Torres and F. Divina and P. M. Martínez-García Application of XAI to the prediction of CTCF binding sites Journal Article In: Results in Engineering, vol. 25, pp. 103776, 2025. @article{vanhaeren2025application,The inherent ‘black box’ nature of deep learning models has hindered their widespread adoption in certain fields, as they provide limited transparency into the reasoning behind their predictions. In the last years, Explainable Artificial Intelligence (XAI) techniques have proven to be effective not only in prediction itself but also in the extraction of meaningful knowledge from deep learning models by means of feature interpretation. In this study, Local Interpretable Model-agnostic Explanations are applied to the prediction of CTCF binding sites, a common task in the field of genomics. Good prediction performances and inferred explanations are obtained that highlight the most informative features that contribute to predictions such as chromatin accessibility and cis-regulatory elements which align well with previously reported data. This work represents a proof of concept showing that XAI are suitable for the extraction of molecular insights from complex biological problems like CTCF binding prediction. |
A. Vellinger and F. Rodriguez-Diaz and F. Divina and J. F. Torres Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning Journal Article In: Logic Journal of the IGPL, vol. to appear, no. to appear, pp. to appear, 2025. @article{Vellinger2025Livestock,In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming. |
M. García-Torres and F. Saucedo and F. Divina and S. Gómez RFMSU: A multivariate symmetrical uncertainty based random forest Journal Article In: Pattern Recognition, vol. 169, pp. 111939, 2025. @article{garcia2025rfmsu,Decision Trees (DTs) have become very popular classifiers due to their good performance and, most of all, their interpretability. In addition, the machine learning community is also paying attention to Random Forests (RFs) since they defy the interpretability-accuracy tradeoff. Most RFs strategies are based on univariate measures, a fact that may limit the capability of identifying the interaction among more than two features. In order to overcome this problem many multivariate approaches have been proposed. However, most of them are based on finding linear or non-linear combinations of features. In this work, we propose a novel univariate RF strategy that builds DTs using the Multivariate Symmetrical Uncertainty (MSU) measure as splitting criterion. The proposal, referred to as RF$_MSU$, was tested on high-dimensional datasets and compared to state-of-the-art univariate and multivariate DTs and RFs classifiers. Results suggest that RF$_MSU$ is capable of finding simpler rules than other RFs approaches while keeping a high predictive power equivalent to that of multivariate approaches. The DT strategies considered obtained simpler models than RF$_MSU$, but at the expense of degrading the classifier. Thus, we can conclude that RFMS U is a RF-based classifier that achieves a good trade-off between the performance and the complexity of the model. |
A. López-Fernández and F. Divina and F. A. Gómez-Vela and M. García-Torres Data mining for enhancing learning and assessment to a microcompetence-based methodology in higher education Journal Article In: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 2025. @article{lopez2025data,This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education. url = https://ieeexplore.ieee.org/abstract/document/10849581 |
2024 |
F. Morales-Mareco and M. García-Torres and F. Divina and D. H Stalder and C. Sauer Machine learning for electric energy consumption forecasting: Application to the Paraguayan system Journal Article In: Logic Journal of the IGPL, pp. jzae035, 2024. @article{morales2024machine,In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions. |
F. Divina and M. García-Torres and F. Gómez-Vela and D. S. Rodriguez-Baena A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach Journal Article In: AIMS Mathematics, vol. 9, no. 5, pp. 13358–13384, 2024. @article{divina2024stacking,Automatic determination of abnormal animal activities can be helpful for the timely detection of signs of health and welfare problems. Usually, this problem is addressed as a classification problem, which typically requires manual annotation of behaviors. This manual annotation can introduce noise into the data and may not always be possible. This motivated us to address the problem as a time-series forecasting problem in which the activity of an animal can be predicted. In this work, different machine learning techniques were tested to obtain activity patterns for Iberian pigs. In particular, we propose a novel stacking ensemble learning approach that combines base learners with meta-learners to obtain the final predictive model. Results confirm the superior performance of the proposed method relative to the other tested strategies. We also explored the possibility of using predictive models trained on an animal to predict the activity of different animals on the same farm. As expected, the predictive performance degrades in this case, but it remains acceptable. The proposed method could be integrated into a monitoring system that may have the potential to transform the way farm animals are monitored, improving their health and welfare conditions, for example, by allowing the early detection of a possible health problem. |
2023 |
A. Vellinger and J. F. Torres and F. Divina and W. Vanhoof Neuroevolutionary Transfer Learning for Time Series Forecasting Conference SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, vol. 749, Lecture Notes in Networks and Systems 2023. @conference{VELLINGER23, |
O. Cardozo and V. Ojeda and R. Parra and J. C. Mello-Román and J. L. Noguera Vázquez and M. García-Torres and F. Divina and S. Grillo and C. Villalba and J. Facon Dataset of fundus images for the diagnosis of ocular toxoplasmosis Journal Article In: Data in Brief, pp. 109056, 2023. @article{cardozo2023dataset,Toxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis. The dataset was developed by three ophthalmologists with expertise in toxoplasmosis detection using fundus images. The dataset will be of great use to researchers working on ophthalmic image analysis using artificial intelligence techniques for the automatic detection of toxoplasmosis chorioretinitis. |
M. García-Torres and R. Ruiz and F. Divina Evolutionary feature selection on high dimensional data using a search space reduction approach Journal Article In: Engineering Applications of Artificial Intelligence, vol. 117, pp. 105556, 2023. @article{garcia2023evolutionary,Feature selection is becoming more and more a challenging task due to the increase of the dimensionality of the data. The complexity of the interactions among features and the size of the search space make it unfeasible to find the optimal subset of features. In order to reduce the search space, feature grouping has arisen as an approach that allows to cluster feature according to the shared information about the class. On the other hand, metaheuristic algorithms have proven to achieve sub-optimal solutions within a reasonable time. In this work we propose a Scatter Search (SS) strategy that uses feature grouping to generate an initial population comprised of diverse and high quality solutions. Solutions are then evolved by applying random mechanisms in combination with the feature group structure, with the objective of maintaining during the search a population of good and, at the same time, as diverse as possible solutions. Not only does the proposed strategy provide the best subset of features found but it also reduces the redundancy structure of the data. We test the strategy on high dimensional data from biomedical and text-mining domains. The results are compared with those obtained by other adaptations of SS and other popular strategies. Results show that the proposed strategy can find, on average, the smallest subsets of features without degrading the performance of the classifier. |
2022 |
G. Velázquez and F. Morales and M. García-Torres and F. Gómez-Vela and F. Divina and J.L. Vázquez Noguera and F. Daumas-Ladouce and C. Ayala and D. Pinto-Roaand P. Gardel-Sotomayor Distribution level Electric current consumption and meteorological data set of the East region of Paraguay Journal Article In: Data in Brief, vol. 40, pp. 107699, 2022. @article{velazquez2022distribution,This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area. |
F. Delgado-Chaves and P. M. Martínez-García and A. Herrero-Ruiz and F. Gómez-Vela and F. Divina and S. Jimeno-González and F. Cortés-Ledesma Data of transcriptional effects of the merbarone-mediated inhibition of TOP2 Journal Article In: Data in Brief, vol. 44, pp. 108499, 2022. @article{delgado2022data,Type II DNA topoisomerases relax topological stress by transiently gating DNA passage in a controlled cut-and-reseal mechanism that affects both DNA strands. Therefore, they are essential to overcome topological problems associated with DNA metabolism. Their aberrant activity results in the generation of DNA double-strand breaks, which can seriously compromise cell survival and genome integrity. Here, we profile the transcriptome of human-telomerase-immortalized retinal pigment epithelial 1 (RPE-1) cells when treated with merbarone, a drug that catalytically inhibits type II DNA topoisomerases. We performed RNA-Seq after 4 and 8 h of merbarone treatment and compared transcriptional profiles versus untreated samples. We report raw sequencing data together with lists of gene counts and differentially expressed genes. |
F. Morales and M. García-Torres and G. Velázquez and F. Daumas-Ladouce and P. Gardel-Sotomayor and F. Gómez-Vela and F. Divina and J. L. Vázquez Noguera and C. Sauer Ayala and D. Pinto-Roa Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study Journal Article In: Electronics, vol. 11, no. 2, pp. 267, 2022. @article{morales2022analysisb,Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters. |
J. A. Gallardo-Gómez and F. Divina and A. Troncoso and F. Martínez-Álvarez Explainable Artificial Intelligence for the Electric Vehicle Load Demand Forecasting Problem Conference SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2022. @conference{gallardo2022explainable, |