A Multiple Classifier System for fast an accurate learning in Neural Network context
Por:
Romero E.F., Valdovinos R.M., Alejo R., Marcial-Romero J.R., Carrasco-Ochoa J.A.
Publicada:
1 ene 2016
Resumen:
Nowadays, the Multiple Classification Systems (MCS) (also called as ensemble of classifiers, committee of learners and mixture of experts) constitutes a well-established research field in Pattern Recognition and Machine Learning. The MCS consists in dividing the whole problem with resampling methods, or using different models for constructing the system over a single data set. A similar approach is studied in the Neural Network context, with the Modular Neural Network. The main difference between these approaches is the processing cost associate to the training step of the Modular Neural Network (in its classical form), due to each module requires to be learned with the whole data set. In this paper, we analyze the performance of a Modular Neural Network and a Multiple Classifier System integrated by small Modular Neural Networks as individual member, in order to identity the convenience of each one. The experiments here were carried out on datasets from real problems showing the effectiveness of the Multiple Classifier System in terms of overall accuracy and processing time respect to uses a single Modular Neural Network. © 2016, CEUR-WS. All rights reserved.
Filiaciones:
Romero E.F.:
Universidad Autónoma del Estado de Mexico, Centro Universitario Valle de Chalco, Hermenegildo Galena #3, Col. Ma. Isabel, Valle de Chalco, Mexico
Valdovinos R.M.:
Universidad Autónoma del Estado de Mexico, Facultad de Ingeniería, Ciudad Universitaria, Cerro de Coatepec s/n, Toluca, Mexico
Alejo R.:
Universidad Autónoma del Estado de Mexico, Tecnológico de Estudios Superiores de Jocotitlán, Carretera Toluca-Atlacomulco km 44.8, Col. Ejido de San Juan y San Agustín, Jocotitlán, Mexico
Marcial-Romero J.R.:
Universidad Autónoma del Estado de Mexico, Facultad de Ingeniería, Ciudad Universitaria, Cerro de Coatepec s/n, Toluca, Mexico
Carrasco-Ochoa J.A.:
Universidad Autónoma del Estado de Mexico, Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico
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