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Alberto Fernández
Researcher at University of Granada
Publications - 213
Citations - 17259
Alberto Fernández is an academic researcher from University of Granada. The author has contributed to research in topics: Fuzzy rule & Fuzzy classification. The author has an hindex of 49, co-authored 206 publications receiving 14158 citations. Previous affiliations of Alberto Fernández include University of Jaén & Central University of Venezuela.
Papers
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
TL;DR: A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.
Proceedings Article
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
Jesús Alcalá-Fdez,Alberto Fernández,Julián Luengo,Joaquín Derrac,Salvador García,Luciano Sánchez,Francisco Herrera +6 more
TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.
Journal ArticleDOI
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
TL;DR: This paper focuses on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence, and presents a case study which involves a set of techniques in classification tasks.
Journal ArticleDOI
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
TL;DR: This work carries out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem, and introduces several approaches and recommendations to address these problems in conjunction with imbalanced data.
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SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
TL;DR: The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data because of its simplicity in the design, as well as its robustness when applied to different type of problems.