Sebastián Ventura | Universidad de Córdoba (original) (raw)

Papers by Sebastián Ventura

Springer eBooks, 2013

In the last years, the learning from multi-label data has attracted significant attention from a ... more In the last years, the learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of modern applications that contain this type of data. Several methods have been proposed for solving this problem, however how to make feature weighting on multi-label data is still lacking in the literature. In multi-label data, each data point can be attributed to multiple labels simultaneously, thus a major difficulty lies in the determinations of the features useful for all multi-label concepts. In this paper, a new method for feature weighting in multi-label learning area is presented, based on the principles of the well-known ReliefF algorithm. The experimental stage shows the effectiveness of the proposal.

Revista Iberoamericana De Tecnologías Del Aprendizaje, Feb 1, 2013

CRC Press eBooks, Oct 25, 2010

Learning. management. systems.(LMSs). can. offer. a. great. variety. of. channels. and. workspace... more Learning. management. systems.(LMSs). can. offer. a. great. variety. of. channels. and. workspaces. to. facilitate. information. sharing. and. communication. among. participants. in. a. course.. They. let. educators. distribute. information. to. students,. produce. content. material,. prepare. assignments. and. tests,. engage. in. discussions,. manage. distance. classes,. and. enable. collaborative. learning. with. forums,. chats,. file. storage. areas,. news. services,. etc.. Some. examples. of. commercial. systems. are. Blackboard.[1],. ...

2022 IEEE Congress on Evolutionary Computation (CEC), Jul 18, 2022

Multi-view learning is a novel paradigm that aims at obtaining better results by examining the in... more Multi-view learning is a novel paradigm that aims at obtaining better results by examining the information from several perspectives instead of by analysing the same information from a single viewpoint. The multi-view methodology has widely been used for semi-supervised learning, where just some patterns were previously classified by an expert and there is a large amount of unlabelled ones. However to our knowledge, the multi-view learning paradigm has not been applied to produce interpretable rule-based classifiers before. In this work, we present a multi-view extension of a grammar-based genetic programming model for inducing rules for semi-supervised contexts. Its idea is to evolve several populations, and their corresponding views, favouring both the accuracy of the predictions for the labelled patterns and the prediction agreement with the other views for unlabelled ones. We have carried out experiments with two to five views, on six common datasets for fully-supervised learning that have been partially anonymised for our semi-supervised study. Our results show that the multi-view paradigm allows to obtain slightly better rule-based classifiers, and that two views becomes preferred.

The reduction of energy consumption in buildings is one of the goals to improve energy efficiency... more The reduction of energy consumption in buildings is one of the goals to improve energy efficiency. One way to achieve energy savings in buildings is to develop intelligent control strategies for heating systems that are able to reduce power consumption without affecting the thermal comfort. An intelligent control system must be able to predict the temperature of the building in order to manage the heating system. In this paper, we present a rule-based model that is able to predict the indoor temperature for different values of k (hours ahead in time). The model has been learned with FRULER, a genetic fuzzy system that generates accurate and simple knowledge bases. Our approach has been validated with real data from a residential college.

Una posible alternativa o complemento a las tecnicas clasicas de evaluacion es la utilizacion de ... more Una posible alternativa o complemento a las tecnicas clasicas de evaluacion es la utilizacion de tecnicas de autoevaluacion (self-grading o self-assessment) que es un proceso en el que es el propio estudiante el que juzga los logros conseguidos respecto a una tarea o actividad determinada. Sin embargo, antes de poder incluirla en un programa educativo, es necesario evaluar la fiabilidad del proceso y compararlo con los metodos tradicionales que actualmente utiliza el profesorado. Siguiendo esta idea, el presente trabajo propone una me-todologia basada en la mineria de datos y la autoevaluacion con el fin de validar la autocalificacion de los estudiantes. Nuestro objetivo es predecir si el instructor podria aceptar o no las calificaciones propuestas por los estudiantes en un curso. Nuestra experiencia empirica sobre 53 estudiantes universitarios de informatica sugiere que son necesarios 1) una distincion en el tipo de errores que puede cometer el clasificador, pues no es lo mismo no acept...

ACM Transactions on Intelligent Systems and Technology, 2018

Multi-label learning has become an important area of research owing to the increasing number of r... more Multi-label learning has become an important area of research owing to the increasing number of real-world problems that contain multi-label data. Data labeling is an expensive process that requires expert handling. The annotation of multi-label data is laborious since a human expert needs to consider the presence/absence of each possible label. Consequently, numerous modern multi-label problems may involve a small number of labeled examples and plentiful unlabeled examples simultaneously. Active learning methods allow us to induce better classifiers by selecting the most useful unlabeled data, thus considerably reducing the labeling effort and the cost of training an accurate model. Batch-mode active learning methods focus on selecting a set of unlabeled examples in each iteration in such a way that the selected examples are informative and as diverse as possible. This article presents a strategy to perform batch-mode active learning on multi-label data. The batch-mode active learn...

IEEE Transactions on Learning Technologies, 2019

Iniciacion a La Investigacion, Sep 15, 2014

This paper proposes to apply data mining techniques to predict school failure and drop out. We us... more This paper proposes to apply data mining techniques to predict school failure and drop out. We use real data on 670 middle-school students from Zacatecas, México and employ white-box classification methods such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or drop out by: firstly, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data, and using cost sensitive classification. The outcomes have been compared and the best resulting models are shown.

Predicting student performance (PSP) is one of the educational data mining task, where we would l... more Predicting student performance (PSP) is one of the educational data mining task, where we would like to know how much knowledge the students have gained and whether they can perform the tasks (or exercises) correctly. Since the student's knowledge improves and cumulates over time, the sequential (temporal) effect is an important information for PSP. Previous works have shown that PSP can be casted as rating prediction task in recommender systems, and therefore, factorization techniques can be applied for this task. To take into account the sequential effect, this work proposes a novel approach which uses tensor factorization for forecasting student performance. With this approach, we can personalize the prediction for each student given the task, thus, it can also be used for recommending the tasks to the students. Experimental results on two large data sets show that incorporating forecasting techniques into the factorization process is a promising approach.

This paper describes a tool that enables instructors to select, visualize and mine students' usag... more This paper describes a tool that enables instructors to select, visualize and mine students' usage data in Moodle courses. The tool has been developed in PHP language and integrated in Moodle as a block.

In this paper we propose a methodology based on data mining and self-evaluation in order to predi... more In this paper we propose a methodology based on data mining and self-evaluation in order to predict whether an instructor will or will not accept the students' proposed marks in a course. This is an ongoing work in which we have evaluated the usage of classification techniques and cost-sensitive corrections. We have carried out several experiments using data gathered from 53 computer science university students.

Neurocomputing, 2015

ABSTRACT Multi-label learning has become an important area of research due to the increasing numb... more ABSTRACT Multi-label learning has become an important area of research due to the increasing number of modern applications that contain multi-label data. The multi-label data are structured in a more complex way than single-label data. Consequently the development of techniques that allow the improvement in the performance of machine learning algorithms over multi-label data is desired. The feature weighting and feature selection algorithms are important feature engineering techniques which have a beneficial impact on the machine learning. The ReliefF algorithm is one of the most popular algorithms to feature estimation and it has proved its usefulness in several domains. This paper presents three extensions of the ReliefF algorithm for working in the multi-label learning context, namely ReliefF-ML, PPT-ReliefF and RReliefF-ML. PPT-ReliefF uses a problem transformation method to convert the multi-label problem into a single-label problem. ReliefF-ML and RReliefF-ML adapt the classic ReliefF algorithm in order to handle directly the multi-label data. The proposed ReliefF extensions are evaluated and compared with previous ReliefF extensions on 34 multi-label datasets. The results show that the proposed ReliefF extensions improve preceding extensions and overcome some of their drawbacks. The experimental results are validated using several nonparametric statistical tests and confirm the effectiveness of the proposal for a better multi-label learning.

Management of hyperglycemia in hospitalized patients has a significant bearing on outcome, in ter... more Management of hyperglycemia in hospitalized patients has a significant bearing on outcome, in terms of both morbidity and mortality. However, there are few national assessments of diabetes care during hospitalization which could serve as a baseline for change. This analysis of a large clinical database (74 million unique encounters corresponding to 17 million unique patients) was undertaken to provide such an assessment and to find future directions which might lead to improvements in patient safety. Almost 70,000 inpatient diabetes encounters were identified with sufficient detail for analysis. Multivariable logistic regression was used to fit the relationship between the measurement of HbA1c and early readmission while controlling for covariates such as demographics, severity and type of the disease, and type of admission. Results show that the measurement of HbA1c was performed infrequently (18.4%) in the inpatient setting. The statistical model suggests that the relationship between the probability of readmission and the HbA1c measurement depends on the primary diagnosis. The data suggest further that the greater attention to diabetes reflected in HbA1c determination may improve patient outcomes and lower cost of inpatient care.

2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012

The definition of similarity metrics is one of the most important tasks in the development of nea... more The definition of similarity metrics is one of the most important tasks in the development of nearest neighbours and instance based learning methods. Furthermore, the performance of lazy algorithms can be significantly improved with the use of an appropriate weight vector. In the last years, the learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of modern applications that contain this type of data. This paper presents a new method for feature weighting, defining a similarity metric as heuristic to estimate the feature weights, and improving the performance of lazy multi-label ranking algorithms. The experimental stage shows the effectiveness of our proposal.

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2010

Advances. in. technology. and. the. impact. of. the. Internet. in. the. last. few. years. have. b... more Advances. in. technology. and. the. impact. of. the. Internet. in. the. last. few. years. have. both. affected. all. aspects. of. our. lives.. In. particular,. the. implications. in. educational. circles. are. of. an. incalculable. magnitude,. making. the. relationship. between. technology. and. education. more. and. more. obvious. and. necessary.. In. this. respect,. it. is. important. to. mention. the. appearance. of. the. virtual. learning. environment.(VLE). or. e-learning. platforms.[1].. These. systems. can. potentially. eliminate. barriers. and. provide. flexibility,. ...

Applied Intelligence, 2014

2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012

Abstract Classification in imbalanced domains is a challenging task, since most of its real domai... more Abstract Classification in imbalanced domains is a challenging task, since most of its real domain applications present skewed distributions of data. However, there are still some open issues in this kind of problem. This paper presents a multi-objective grammar-based ant programming algorithm for imbalanced classification, capable of addressing this task from both the binary and multiclass sides, unlike most of the solutions presented so far. We carry out two experimental studies comparing our algorithm against binary and multiclass ...

Springer eBooks, 2013

In the last years, the learning from multi-label data has attracted significant attention from a ... more In the last years, the learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of modern applications that contain this type of data. Several methods have been proposed for solving this problem, however how to make feature weighting on multi-label data is still lacking in the literature. In multi-label data, each data point can be attributed to multiple labels simultaneously, thus a major difficulty lies in the determinations of the features useful for all multi-label concepts. In this paper, a new method for feature weighting in multi-label learning area is presented, based on the principles of the well-known ReliefF algorithm. The experimental stage shows the effectiveness of the proposal.

Revista Iberoamericana De Tecnologías Del Aprendizaje, Feb 1, 2013

CRC Press eBooks, Oct 25, 2010

Learning. management. systems.(LMSs). can. offer. a. great. variety. of. channels. and. workspace... more Learning. management. systems.(LMSs). can. offer. a. great. variety. of. channels. and. workspaces. to. facilitate. information. sharing. and. communication. among. participants. in. a. course.. They. let. educators. distribute. information. to. students,. produce. content. material,. prepare. assignments. and. tests,. engage. in. discussions,. manage. distance. classes,. and. enable. collaborative. learning. with. forums,. chats,. file. storage. areas,. news. services,. etc.. Some. examples. of. commercial. systems. are. Blackboard.[1],. ...

2022 IEEE Congress on Evolutionary Computation (CEC), Jul 18, 2022

Multi-view learning is a novel paradigm that aims at obtaining better results by examining the in... more Multi-view learning is a novel paradigm that aims at obtaining better results by examining the information from several perspectives instead of by analysing the same information from a single viewpoint. The multi-view methodology has widely been used for semi-supervised learning, where just some patterns were previously classified by an expert and there is a large amount of unlabelled ones. However to our knowledge, the multi-view learning paradigm has not been applied to produce interpretable rule-based classifiers before. In this work, we present a multi-view extension of a grammar-based genetic programming model for inducing rules for semi-supervised contexts. Its idea is to evolve several populations, and their corresponding views, favouring both the accuracy of the predictions for the labelled patterns and the prediction agreement with the other views for unlabelled ones. We have carried out experiments with two to five views, on six common datasets for fully-supervised learning that have been partially anonymised for our semi-supervised study. Our results show that the multi-view paradigm allows to obtain slightly better rule-based classifiers, and that two views becomes preferred.

The reduction of energy consumption in buildings is one of the goals to improve energy efficiency... more The reduction of energy consumption in buildings is one of the goals to improve energy efficiency. One way to achieve energy savings in buildings is to develop intelligent control strategies for heating systems that are able to reduce power consumption without affecting the thermal comfort. An intelligent control system must be able to predict the temperature of the building in order to manage the heating system. In this paper, we present a rule-based model that is able to predict the indoor temperature for different values of k (hours ahead in time). The model has been learned with FRULER, a genetic fuzzy system that generates accurate and simple knowledge bases. Our approach has been validated with real data from a residential college.

Una posible alternativa o complemento a las tecnicas clasicas de evaluacion es la utilizacion de ... more Una posible alternativa o complemento a las tecnicas clasicas de evaluacion es la utilizacion de tecnicas de autoevaluacion (self-grading o self-assessment) que es un proceso en el que es el propio estudiante el que juzga los logros conseguidos respecto a una tarea o actividad determinada. Sin embargo, antes de poder incluirla en un programa educativo, es necesario evaluar la fiabilidad del proceso y compararlo con los metodos tradicionales que actualmente utiliza el profesorado. Siguiendo esta idea, el presente trabajo propone una me-todologia basada en la mineria de datos y la autoevaluacion con el fin de validar la autocalificacion de los estudiantes. Nuestro objetivo es predecir si el instructor podria aceptar o no las calificaciones propuestas por los estudiantes en un curso. Nuestra experiencia empirica sobre 53 estudiantes universitarios de informatica sugiere que son necesarios 1) una distincion en el tipo de errores que puede cometer el clasificador, pues no es lo mismo no acept...

ACM Transactions on Intelligent Systems and Technology, 2018

Multi-label learning has become an important area of research owing to the increasing number of r... more Multi-label learning has become an important area of research owing to the increasing number of real-world problems that contain multi-label data. Data labeling is an expensive process that requires expert handling. The annotation of multi-label data is laborious since a human expert needs to consider the presence/absence of each possible label. Consequently, numerous modern multi-label problems may involve a small number of labeled examples and plentiful unlabeled examples simultaneously. Active learning methods allow us to induce better classifiers by selecting the most useful unlabeled data, thus considerably reducing the labeling effort and the cost of training an accurate model. Batch-mode active learning methods focus on selecting a set of unlabeled examples in each iteration in such a way that the selected examples are informative and as diverse as possible. This article presents a strategy to perform batch-mode active learning on multi-label data. The batch-mode active learn...

IEEE Transactions on Learning Technologies, 2019

Iniciacion a La Investigacion, Sep 15, 2014

This paper proposes to apply data mining techniques to predict school failure and drop out. We us... more This paper proposes to apply data mining techniques to predict school failure and drop out. We use real data on 670 middle-school students from Zacatecas, México and employ white-box classification methods such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or drop out by: firstly, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data, and using cost sensitive classification. The outcomes have been compared and the best resulting models are shown.

Predicting student performance (PSP) is one of the educational data mining task, where we would l... more Predicting student performance (PSP) is one of the educational data mining task, where we would like to know how much knowledge the students have gained and whether they can perform the tasks (or exercises) correctly. Since the student's knowledge improves and cumulates over time, the sequential (temporal) effect is an important information for PSP. Previous works have shown that PSP can be casted as rating prediction task in recommender systems, and therefore, factorization techniques can be applied for this task. To take into account the sequential effect, this work proposes a novel approach which uses tensor factorization for forecasting student performance. With this approach, we can personalize the prediction for each student given the task, thus, it can also be used for recommending the tasks to the students. Experimental results on two large data sets show that incorporating forecasting techniques into the factorization process is a promising approach.

This paper describes a tool that enables instructors to select, visualize and mine students' usag... more This paper describes a tool that enables instructors to select, visualize and mine students' usage data in Moodle courses. The tool has been developed in PHP language and integrated in Moodle as a block.

In this paper we propose a methodology based on data mining and self-evaluation in order to predi... more In this paper we propose a methodology based on data mining and self-evaluation in order to predict whether an instructor will or will not accept the students' proposed marks in a course. This is an ongoing work in which we have evaluated the usage of classification techniques and cost-sensitive corrections. We have carried out several experiments using data gathered from 53 computer science university students.

Neurocomputing, 2015

ABSTRACT Multi-label learning has become an important area of research due to the increasing numb... more ABSTRACT Multi-label learning has become an important area of research due to the increasing number of modern applications that contain multi-label data. The multi-label data are structured in a more complex way than single-label data. Consequently the development of techniques that allow the improvement in the performance of machine learning algorithms over multi-label data is desired. The feature weighting and feature selection algorithms are important feature engineering techniques which have a beneficial impact on the machine learning. The ReliefF algorithm is one of the most popular algorithms to feature estimation and it has proved its usefulness in several domains. This paper presents three extensions of the ReliefF algorithm for working in the multi-label learning context, namely ReliefF-ML, PPT-ReliefF and RReliefF-ML. PPT-ReliefF uses a problem transformation method to convert the multi-label problem into a single-label problem. ReliefF-ML and RReliefF-ML adapt the classic ReliefF algorithm in order to handle directly the multi-label data. The proposed ReliefF extensions are evaluated and compared with previous ReliefF extensions on 34 multi-label datasets. The results show that the proposed ReliefF extensions improve preceding extensions and overcome some of their drawbacks. The experimental results are validated using several nonparametric statistical tests and confirm the effectiveness of the proposal for a better multi-label learning.

Management of hyperglycemia in hospitalized patients has a significant bearing on outcome, in ter... more Management of hyperglycemia in hospitalized patients has a significant bearing on outcome, in terms of both morbidity and mortality. However, there are few national assessments of diabetes care during hospitalization which could serve as a baseline for change. This analysis of a large clinical database (74 million unique encounters corresponding to 17 million unique patients) was undertaken to provide such an assessment and to find future directions which might lead to improvements in patient safety. Almost 70,000 inpatient diabetes encounters were identified with sufficient detail for analysis. Multivariable logistic regression was used to fit the relationship between the measurement of HbA1c and early readmission while controlling for covariates such as demographics, severity and type of the disease, and type of admission. Results show that the measurement of HbA1c was performed infrequently (18.4%) in the inpatient setting. The statistical model suggests that the relationship between the probability of readmission and the HbA1c measurement depends on the primary diagnosis. The data suggest further that the greater attention to diabetes reflected in HbA1c determination may improve patient outcomes and lower cost of inpatient care.

2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012

The definition of similarity metrics is one of the most important tasks in the development of nea... more The definition of similarity metrics is one of the most important tasks in the development of nearest neighbours and instance based learning methods. Furthermore, the performance of lazy algorithms can be significantly improved with the use of an appropriate weight vector. In the last years, the learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of modern applications that contain this type of data. This paper presents a new method for feature weighting, defining a similarity metric as heuristic to estimate the feature weights, and improving the performance of lazy multi-label ranking algorithms. The experimental stage shows the effectiveness of our proposal.

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2010

Advances. in. technology. and. the. impact. of. the. Internet. in. the. last. few. years. have. b... more Advances. in. technology. and. the. impact. of. the. Internet. in. the. last. few. years. have. both. affected. all. aspects. of. our. lives.. In. particular,. the. implications. in. educational. circles. are. of. an. incalculable. magnitude,. making. the. relationship. between. technology. and. education. more. and. more. obvious. and. necessary.. In. this. respect,. it. is. important. to. mention. the. appearance. of. the. virtual. learning. environment.(VLE). or. e-learning. platforms.[1].. These. systems. can. potentially. eliminate. barriers. and. provide. flexibility,. ...

Applied Intelligence, 2014

2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012

Abstract Classification in imbalanced domains is a challenging task, since most of its real domai... more Abstract Classification in imbalanced domains is a challenging task, since most of its real domain applications present skewed distributions of data. However, there are still some open issues in this kind of problem. This paper presents a multi-objective grammar-based ant programming algorithm for imbalanced classification, capable of addressing this task from both the binary and multiclass sides, unlike most of the solutions presented so far. We carry out two experimental studies comparing our algorithm against binary and multiclass ...