Person:
Chacón Maldonado, Andrés Manuel

Titulado superior de apoyo a la investigación
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First Name
Andrés Manuel
Last Name
Chacón Maldonado
Affiliation
Universidad Pablo de Olavide
Department
Deporte e Informática
Research Center
Area
Lenguajes y Sistemas Informáticos
Research Group
PAIDI Areas
PhD programs
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Now showing 1 - 2 of 2
  • Publication
    FS-Studio: An extensive and efficient feature selection experimentation tool for Weka Explorer
    (Elsevier, 2023) Chacón Maldonado, Andrés Manuel; Asencio Cortes, Gualberto; Martínez-Álvarez, Francisco; Troncoso, Alicia
    A new tool with a friendly graphical user interface specifically designed to perform feature selection experiments in Weka Explorer allowing parallel computation is proposed in this work. The proposed tool performs Bayesian statistical tests among the selected feature selection techniques to check whether the differences are statistically significant or not. Moreover, the recently published general- purpose metaheuristic named Coronavirus Optimization Algorithm is also adapted for feature selection and integrated in the proposed tool to search for attribute subsets, allowing its use along with any Weka attribute subset evaluation algorithm. After the feature selection process is performed, both classification and regression techniques can be applied to the dataset built with the most relevant features. Finally, the output of the whole process is sent to an exportable table, customizable by means of a bar plot, in order to gather both predicted and actual values as well as the evaluation metrics.
  • Publication
    Earthquake Prediction in California Using Feature Selection Techniques
    (Springer, Cham, 2021-09-23) Roiz-Pagador, J.; Chacón Maldonado, Andrés Manuel; Ruiz, R.; Asencio Cortés, Gualberto
    Predicting the magnitude of earthquakes is of vital importance and, at the same time, of extreme complexity, where each attribute contributes differently in the process, even introducing noise. Preprocessing using attribute selection techniques helps to alleviate this drawback. In this work, this is demonstrated through an extensive comparison of 47 years of data from the Northern California Earthquake Data Center, where a wide range of feature selection algorithms are applied composed by different search, like population, local and ranking search based; and evaluators, like Correlations, consistency and distance metrics. After that, prediction algorithms will allow to compare the result with and without the application of feature selection, showing that the number of existing attributes can be reduced by 80%, improving metrics of the original, ensuring that the use of attribute selection in this type of problem is quite promising.