Alejandro Rosales-Pérez

Bio: Alejandro Rosales-Pérez is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Evolutionary algorithm & Multi-objective optimization. The author has an hindex of 11, co-authored 28 publications receiving 334 citations. Previous affiliations of Alejandro Rosales-Pérez include National Institute of Astrophysics, Optics and Electronics & Centro de Investigación en Matemáticas.

Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

Abstract: Extending binary ensemble techniques to multi-class imbalanced data.OVO scheme enhancement for multi-class imbalanced data by ensemble learning.A complete experimental study of comparison of the ensemble learning techniques with OVO.Study of the impact of base classifiers used in the proposed scenario. Multi-class imbalance classification problems occur in many real-world applications, which suffer from the quite different distribution of classes. Decomposition strategies are well-known techniques to address the classification problems involving multiple classes. Among them binary approaches using one-vs-one and one-vs-all has gained a significant attention from the research community. They allow to divide multi-class problems into several easier-to-solve two-class sub-problems. In this study we develop an exhaustive empirical analysis to explore the possibility of empowering the one-vs-one scheme for multi-class imbalance classification problems with applying binary ensemble learning approaches. We examine several state-of-the-art ensemble learning methods proposed for addressing the imbalance problems to solve the pairwise tasks derived from the multi-class data set. Then the aggregation strategy is employed to combine the binary ensemble outputs to reconstruct the original multi-class task. We present a detailed experimental study of the proposed approach, supported by the statistical analysis. The results indicate the high effectiveness of ensemble learning with one-vs-one scheme in dealing with the multi-class imbalance classification problems.

An Evolutionary Multiobjective Model and Instance Selection for Support Vector Machines With Pareto-Based Ensembles

Abstract: Support vector machines (SVMs) are among the most powerful learning algorithms for classification tasks. However, these algorithms require a high computational cost during the training phase, which can limit their application on large-scale datasets. Moreover, it is known that their effectiveness highly depends on the hyper-parameters used to train the model. With the intention of dealing with these, this paper introduces an evolutionary multiobjective model and instance selection (IS) approach for SVMs with Pareto-based ensemble, whose goals are, precisely, to optimize the size of the training set and the classification performance attained by the selection of the instances, which can be done using either a wrapper or a filter approach. Due to the nature of multiobjective evolutionary algorithms, several Pareto optimal solutions can be found. We study several ways of using such information to perform a classification task. To accomplish this, our proposal performs a processing over the Pareto solutions in order to combine them into a single ensemble. This is done in five different ways, which are based on: 1) a global Pareto ensemble; 2) error reduction; 3) a complementary error reduction; 4) maximized margin distance; and 5) boosting. Through a comprehensive experimental study we evaluate the suitability of the proposed approach and the Pareto processing, and we show its advantages over a single-objective formulation, traditional IS techniques, and learning algorithms.

A hybrid surrogate-based approach for evolutionary multi-objective optimization

Abstract: Evolutionary algorithms have gained popularity as an alternative for dealing with multi-objective optimization problems. However, these algorithms require to perform a relatively high number of fitness function evaluations in order to generate a reasonably good approximation of the Pareto front. This can be a shortcoming when fitness evaluations are computationally expensive. In this paper, we propose an approach that combines an evolutionary algorithm with an ensemble of surrogate models based on support vector machines (SVM), which are used to approximate the fitness functions of a problem. The proposed approach performs a model selection process for determining the appropriate hyperparameters values for each SVM in the ensemble. The ensemble is constructed in an incremental fashion, such that the models are updated with the knowledge gained during the evolutionary process, but the information from previous evaluated regions is also preserved. A criterion based on surrogate fidelity is also proposed for determining when should the surrogates be updated. We evaluate the performance of our proposal using a benchmark of test problems widely used in the literature and we compare our results with respect to those obtained by the NSGA-II. Our proposed approach is able to significantly reduce the number of fitness function evaluations performed, while producing solutions which are close to the true Pareto front.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Learning from class-imbalanced data

Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Improving Metaheuristic Algorithms With Information Feedback Models

Abstract: In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used in the later optimization process, the quality of the succeeding solutions would be improved significantly. This paper presents our method for reusing the valuable information available from previous individuals to guide later search. In our approach, previous useful information was fed back to the updating process. We proposed six information feedback models. In these models, individuals from previous iterations were selected in either a fixed or random manner. Their useful information was incorporated into the updating process. Accordingly, an individual at the current iteration was updated based on the basic algorithm plus some selected previous individuals by using a simple fitness weighting method. By incorporating six different information feedback models into ten metaheuristic algorithms, this approach provided a number of variants of the basic algorithms. We demonstrated experimentally that the variants outperformed the basic algorithms significantly on 14 standard test functions and 10 CEC 2011 real world problems, thereby, establishing the value of the information feedback models.