O
Octavio Loyola-González
Researcher at Monterrey Institute of Technology and Higher Education
Publications - 49
Citations - 996
Octavio Loyola-González is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Computer science & Minutiae. The author has an hindex of 12, co-authored 41 publications receiving 555 citations. Previous affiliations of Octavio Loyola-González include University of Ciego de Ávila & National Institute of Astrophysics, Optics and Electronics.
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Journal ArticleDOI
Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View
TL;DR: Both explainable and black-box models are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model.
Journal ArticleDOI
Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases
Octavio Loyola-González,José Fco. Martínez-Trinidad,Jesús Ariel Carrasco-Ochoa,Milton García-Borroto +3 more
TL;DR: This study presents a guide based on the class imbalance ratio for selecting a resampling method that jointly with a contrast pattern based classifier allows us to have good results in a class imbalance problem and provides a rough guide for selecting the best resamplings method regarding the IR.
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A one-class classification approach for bot detection on Twitter
Jorge Rodríguez-Ruiz,Javier Israel Mata-Sanchez,Raúl Monroy,Octavio Loyola-González,Armando López-Cuevas +4 more
TL;DR: This paper proposes to use one-class classification to enhance Twitter bot detection, as this allows detecting novel bot accounts, and requires only from examples of legitimate accounts, without requiring previous information about them.
Journal ArticleDOI
PBC4cip: A new contrast pattern-based classifier for class imbalance problems
Octavio Loyola-González,Miguel Angel Medina-Pérez,José Fco. Martínez-Trinidad,Jesús Ariel Carrasco-Ochoa,Raúl Monroy,Milton García-Borroto +5 more
TL;DR: From the experimental results, it can be concluded that the proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems.
Journal ArticleDOI
Contrast Pattern-Based Classification for Bot Detection on Twitter
Octavio Loyola-González,Raúl Monroy,Jorge Rodríguez,Armando López-Cuevas,Javier Israel Mata-Sanchez +4 more
TL;DR: A pattern-based classification mechanism is used to social bot detection, specifically for Twitter, and a new feature model is introduced, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis.