# José A. GámezUniversity of Castilla-La Mancha · Computer Systems

José A. Gámez

## About

187

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## Publications

Publications (187)

The partial label ranking problem is a general interpretation of the preference learning scenario known as the label ranking problem, the goal of which is to learn preference classifiers able to predict a complete ranking with ties over the finite set of labels of the class variable. In this paper, we use unsupervised discretization techniques (equ...

We propose a new oblique decision tree algorithm based on support vector machines. Our algorithm produces a single model for a multi-class target variable. On the contrary to previous works that manage the multi-class problem by using clustering at each split, we test all the one-vs-rest labels at each split, choosing the one which minimizes an imp...

The Optimal Bucket Order Problem (OBOP) is a rank aggregation problem which consists in finding a consensus ranking (with ties) that generalizes a set of input rankings. In this paper, with the aim of solving the OBOP in an efficient and scalable way, we propose several greedy algorithms based on different sort-first and cluster-second strategies....

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based al...

Bayesian Network (BN) fusion provides a precise theoretical framework for aggregating the graphical structure of a set of BNs into a consensus network. The fusion process depends on a total ordering of the variables, but both the problem of searching for an optimal consensus structure (according to the standard problem definition) as well as the on...

The Label Ranking problem consists in learning preference models from training datasets labeled with a ranking of class labels, and the goal is to predict a ranking for a given unlabeled instance. In this work, we focus on the particular case where both, the training dataset and the prediction given as output allow tied labels (i.e., there is no pa...

The Label Ranking (LR) problem is a well‐known nonstandard supervised classification problem, the goal of which is to learn preference classifiers from data, mapping instances to rankings of the labels of the class variable. In the literature, the particular setting where the output of the LR problem is a complete ranking without ties (a.k.a. permu...

Label Ranking (LR) is a well-known non-standard supervised classification problem, with the goal of inducing preference models able to predict a ranking (permutation) over a finite set of labels from datasets in which the instances are explicitly labelled with (possibly) incomplete rankings. In this work, we focus on the Partial Label Ranking (PLR)...

In the last few years, the platforms for online learning, such as MOOCs, are becoming more and more popular. Particularly, in fields like computer science, students very often choose this way, instead of official programs, to complete their formation. In this context, universities must adapt to changes in order to offer the kind of formation that i...

The main goal of this article is to improve the results obtained by the GLAD algorithm in cases with large data. This algorithm is able to learn from instances labeled by multiple annotators taking into account both the quality of the annotators and the difficulty of the instances. Despite its many advantages, this study shows that GLAD does not sc...

Crowdsourcing opens the door to solving a wide variety of problems that previously were unfeasible in the field of machine learning, allowing us to obtain relatively low cost labeled data in a small amount of time. However, due to the uncertain quality of labelers, the data to deal with are sometimes unreliable, forcing practitioners to collect inf...

The problem of aggregating several rankings in order to obtain a consensus ranking that generalizes them is an active field of research with several applications. The Optimal Bucket Order Problem (OBOP) is a rank aggregation problem where the resulting ranking may be partial, i.e. ties are allowed. Several algorithms have been proposed for OBOP. Ho...

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict ac...

Regression trees (RTs) are simple, but powerful models, which have been widely used in the last decades in different scopes. Fuzzy RTs (FRTs) add fuzziness to RTs with the aim of dealing with uncertain environments. Most of the FRT learning approaches proposed in the literature aim to improve the accuracy, measured in terms of mean squared error, a...

In this article, we propose an improvement over the GLAD algorithm that increases the efficiency and accuracy of the model when working on problems with large datasets. The GLAD algorithm allows practitioners to learn from instances labeled by multiple annotators, taking into account the quality of their annotations and the instance difficulty. How...

Annual journal rankings are usually considered a tool for the evaluation of research and researchers. Although they are an objective resource for such evaluation, they also present drawbacks: (a) the uncertainty about the definite position of a target journal in the corresponding annual ranking when selecting a journal, and (b) in spite of the nons...

The optimal bucket order problem consists in obtaining a complete consensus ranking (ties are allowed) from a matrix of preferences (possibly obtained from a database of rankings). In this paper, we tackle this problem by using (1+λ) evolution strategies. We designed specific mutation operators which are able to modify the inner structure of the bu...

Encouraged by the success of applying metaheuristics algorithms to other ranking-based problems (Kemeny ranking problem and parameter estimation for Mallows distributions), in this paper we deal with the rank aggregation problem (RAP), which can be viewed as a generalization of the Kemeny problem to arbitrary rankings. While in the Kemeny problem t...

Bayesian networks learning is computationally expensive even in the case of sacrificing the optimality of the result. Many methods aim at obtaining quality solutions in affordable times. Most of them are based on local search algorithms, as they allow evaluating candidate networks in a very efficient way, and can be further improved by using local...

Learning fuzzy rule-based systems entails searching a set of fuzzy rules which fits the training data. Even if using fix fuzzy partitions, the amount of rules that can be formed is exponential in the number of variables. Thus, the search must be carried out by means of metaheuristics such as genetic algorithms, and sometimes restricted to the set o...

The multilabel paradigm has recently attracted the attention of the machine learning community, multilabel problems being those which do not have only one class but several binomial classes instead. Although intensive research has been carried on lately into the multilabel classification paradigm, this is not the case of feature subset selection me...

This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy. Considering that: (1) the task of finding an optimal hyperplane with real-valued coefficients is a complex optimization problem in a continuous space, and (2) metaheuristics have been successfully...

This paper deals with group decision making and, in particular, with rank aggregation, which is the problem of aggregating individual preferences (rankings) in order to obtain a consensus ranking. Although this consensus ranking is usually a permutation of all the ranked items, in this paper we tackle the situation in which some items can be tied,...

Solving a problem by using metaheuristic algorithms requires the evaluation of a large number of potential solutions. This paper presents a theoretical and experimental study of the application of partial evaluation in the Rank Aggregation Problem (RAP). Partial evaluation just computes the part of the objective function that is affected by the mod...

High-Efficiency Video Coding (HEVC) was conceived by the Joint Collaborative Team on Video Coding as the natural successor of the H.264/AVC standard, which has been the most extended digital video standard in all segments of the domestic and professional markets for over 10 years. HEVC roughly doubles the compression performance of H.264/AVC in Rat...

Bayesian networks have been widely used for classification problems. These models, structure of the
network and/or its parameters (probability distributions), are usually built from a data set. Sometimes we
do not have information about all the possible values of the class variable, e.g. data about a reactor failure
in a nuclear power station. This...

Several authors have ton the importance of aggregating the results of different feature selection methods in order to improve the solutions obtained. To the best of our knowledge, the consensus rankings obtained in all of these proposals do not allow that some variables are tied. This paper studies the advantages of allowing ties in the consensus r...

This paper deals with the rank aggregation problem in a general setting; in particular, we approach the problem for any kind of ranking: complete or incomplete and with or without ties. The underlying idea behind our approach is to take into account the so-called extension set of a ranking, that is, the set of permutations that are compatible with...

Preference learning is the branch of machine learning in charge of inducing preference models from data. In this paper we focus on the task known as label ranking problem, whose goal is to predict a ranking among the different labels the class variable can take. Our contribution is twofold: (i) taking as basis the tree-based algorithm LRT described...

The challenge of scalability has always been a focus on Machine Learning research, where improved algorithms and new techniques are proposed in a constant basis to deal with more complex problems. With the advent of Big Data, this challenge has been intensified, in which new large scale datasets overwhelm the majority of available techniques. The c...

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensi...

The study of energy efficiency in buildings is an active field of research. Modeling and prediction of power-related magnitudes allow us to analyse the electrical consumption. This can lead to environmental and economical benefits. In this study we compare different techniques to predict active power consumed by buildings of the University of León...

This book constitutes the refereed proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015, held in Albacete, Spain, in November 2015.
The 31 revised full papers presented were carefully selected from 175 submissions. The papers are organized in topical sections on Bayesian networks and uncertainty mo...

This paper deals with the problem of parameter estimation in the generalized Mallows model (GMM) by using both local and global search metaheuristic (MH) algorithms. The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings/permutations. Several approaches can be found in the literature, some of which are...

In Data Mining there is a constant need to provide more scalable tools in order to tackle new domains with an increased level of complexity. Over the last few years one of the main challenges in this field is the growing size of the available data; owing to the level of data generation and storage capacities provided by new emergent technology, a r...

Structural learning of Bayesian networks is a very expensive task even when sacrifying the optimality of the result. Because of that, there are some proposals aimed at obtaining relative-quality solutions in short times. One of them, namely Chain-ACO, searches an ordering among all variables with Ant Colony Optimization and a chain-structured surro...

We present a general framework for multidimensional classification that captures the pairwise interactions between class variables. The pairwise class interactions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random field that is subsequently used for multidimensional inferen...

The study of energy efficiency in buildings is an active field of research. Modelling and predicting energy related magnitudes leads to analyse electric power consumption and can achieve economical benefits. In this study, two machine learning techniques are applied to predict active power in buildings. The real data acquired corresponds to time, e...

Dependency networks have previously been proposed as alternatives to e.g. Bayesian net- works by supporting fast algorithms for automatic learning. Recently dependency net- works have also been proposed as classification models, but as with e.g. general proba- bilistic inference, the reported speed-ups are often obtained at the expense of accuracy....

High Efficiency Video Coding was developed by the JCT-VC to replace the current H.264/AVC standard, which has dominated digital video services in all segments of the domestic and professional markets for over ten years. Therefore, there is a lot of legacy content encoded with H.264/AVC, and an efficient video transcoding from H.264 to HEVC will be...

High Efficiency Video Coding (HEVC) was developed by the Joint Collaborative Team on Video Coding to replace the current H.264/Advanced Video Coding (AVC) standard, which has dominated digital video services in all segments of the domestic and professional markets for over ten years. Therefore, there is a lot of legacy content encoded with H.264/AV...

The study of energy efficiency in buildings is an active field of research. Modelling and predicting energy related magnitudes leads to analyse electric power consumption and can achieve economical benefits. In this study, machine learning techniques are applied to predict active power in buildings. The real data acquired corresponds to time, envir...

In this paper we present a study on the behaviour of some representative Bayesian Networks Classifiers
(BNCs), when the dataset they are learned from presents imbalanced data, that is, there are far fewer cases
labelled with a particular class value than with the other ones (assuming binary classification problems). This
is a typical source of trou...

High efficiency video coding (HEVC) was developed by the Joint Collaborative Team on video coding to replace the current H.264/AVC standard, which has been widely adopted over the last few years. Therefore, there is a lot of legacy content encoded with H.264/AVC, and an efficient conversion to HEVC is needed. This paper presents a hybrid transcodin...

Learning Bayesian networks is known to be an NP-hard problem, and this, combined with the growing interest in learning models from high-dimensional domains, leads to the necessity of finding more efficient learning algorithms. Recent papers have proposed constrained approaches of successfully and widely used local search algorithms, such as Hill Cl...

The High Efficiency Video Coding (HEVC) was developed by the Joint Collaborative Team on Video Coding (JCT-VC) to replace the current H.264/AVC standard which has been widely adopted in the last years. Therefore, there is a lot of legacy content encoded with H.264/AVC and an efficient conversion to HEVC is needed. This paper, presents a Fast Quadtr...

We present a general framework for multidimensional classification that captures the pairwise interactions between class variables. The pairwise class interactions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random field that is subsequently used for multi-label inference (P...

Algorithms for deriving the rule base in Linguistic Fuzzy-Rule Based Systems usually proceed by selecting a set of candidate rules and, afterwards, finding both a subset of them and a combination of values for their consequents. Because of its cost, the latter process can be approached by using metaheuristic techniques such as genetic algorithms. H...

Algorithms which learn Linguistic Fuzzy Rule-Based Systems from data usually start up from the definition of the linguistic variables, generate a set of candidate rules and, afterwards, search a subset of them through a metaheuristic technique. In high-dimensional datasets the number of candidate rules is intractable, and a preselection is a must....

In this paper, we analyze the problem of data clustering in domains where discrete and continuous variables coexist. We propose the use of hybrid Bayesian networks with naïve Bayes structure and hidden class variable. The model integrates discrete and continuous features, by representing the conditional distributions as mixtures of truncated expone...

The motivation for this paper comes from observing the recent tendency to assert that rather than a unique and globally superior classifier, there exist local winners. Hence, the proposal of new classifiers can be seen as an attempt to cover new areas of the complexity space of datasets, or even to compete with those previously assigned to others....

This paper deals with the problem of wrapper feature subset selection (FSS) in classification-oriented datasets with a (very) large number of attributes. In high-dimensional datasets with thousands of variables, wrapper FSS becomes a laborious computational process because of the amount of CPU time it requires. In this paper we study how under cert...

In this paper we describe a system designed for assisting geneticists in vegetal genetic improvement tasks. The system is based on the use of Bayesian networks. It has been developed under the industrial demands emerging from the area of Campo de Dalías in Almería (Spain), and is therefore oriented to producing new tomato varieties, which constitut...

Probabilistic reasoning and learning with permutation data has gained interest in recent years because its use in different ranking-based real-world applications. Therefore, constructing a model from a given set of permutations or rankings has become a target problem in the machine learning community. In this paper we focus on probabilistic modelli...

Learning Bayesian networks is known to be an NP-hard problem, this, combined with the growing interest in learning models from high-dimensional domains, leads to the necessity of finding more efficient learning algorithms. Recent papers propose constrained approaches of successfully and widely used local search algorithms, such as hill climbing. On...

High-radix switches reduce network cost and improve network performance, especially in large switch-based interconnection networks. However, there are some problems related to the integration scale to implement such switches in a single chip. An interesting alternative for building high-radix switches consists of combining several current smaller s...

We propose the use of a genetic algorithm in order to solve the rank aggregation problem, which consists in, given a dataset of rankings (or permutations) of n objects, finding the ranking which best represents such dataset. Though different probabilistic models have been proposed to tackle this problem (see e.g. [12]), the so called Mallows model...

Whereas there exist questionnaires used to measure the level of anxiety or depression in caregivers of schizophrenia patients, sometimes these symptoms take too long to be detected and the treatment needed is more difficult than it would have been if the burden had been detected at an earlier stage. In this paper we propose the use of automatic cla...

Java source code of algorithm proposed in my papers:
Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking.
Pablo Bermejo, Luis de la Ossa, José A. Gámez, José Miguel Puerta
Knowledge-Based Systems 01/2012; 25:35-44.
and
Improving Incremental Wrapper-Based Feature Subset Selection by Using Re-ranking.
Pa...

Java source code for the proposed algorithm in my paper:
Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets.
Pablo Bermejo, José A. Gámez, Jose Miguel Puerta
Expert Systems with Applications 01/2011; 38:2072-2080.

Java source code of the algorithm proposed in my paper:
Improving Incremental Wrapper-Based Subset Selection via Replacement and Early Stopping.
Pablo Bermejo, José A. Gámez, Jose Miguel Puerta
International Journal of Pattern Recognition and Artificial Intelligence 01/2011; 25:605-625.

It is well known that learning Bayesian networks from data is an NP-hard problem. For this reason, usually metaheuristics or approximate algorithms have been used to provide a good solution. In particular, the family of hill climbing algorithms has a key role in this scenario because of its good trade-off between computational demand and the qualit...

Most methods of exact probability propagation in Bayesian networks do not
carry out the inference directly over the network, but over a secondary
structure known as a junction tree or a join tree (JT). The process of
obtaining a JT is usually termed {sl compilation}. As compilation is usually
viewed as a whole process; each time the network is modi...

Probabilistic Decision Graphs (PDGs) are probabilistic graphical models that represent a factorisation of a discrete joint probability distribution using a “decision graph”-like structure over local marginal parameters. The structure of a PDG enables the model to capture some context specific independence relations that are not representable in the...

Indoor thermal comfort is the most commonly studied type of comfort in the literature. We can find works which try to predict the user's satisfaction or keep static conditions by means of black-box controllers. However, we propose a novel system which is capable of adapting to the user's thermal preferences without any prior knowledge, and measurin...

There is still lack of clarity about the best manner in which to handle numeric attributes when applying Bayesian network classifiers. Discretization methods entail an unavoidable loss of information. Nonetheless, a number of studies have shown that appropriate discretization can outperform straightforward use of common, but often unrealistic param...

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fin...

Bayesian Network classifiers (BNCs) are Bayesian Network (BN) models specifically tailored for classification tasks. There is a wide range of existing models that vary in complexity and efficiency. All of them have in common the ability to deal with uncertainty in a very natural way, at the same time providing a descriptive environment. In this cha...