A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

Authors:
R. Alejo;R. M. Valdovinos;V. GarcíA;J. H. Pacheco-Sanchez
Affiliations:
Tecnológico de Estudios Superiores de Jocotitlán, Carretera Toluca-Atlacomulco KM. 44.8, Col. Ejido de San Juan y San Agustín, 50700 Jocotitlán, Mexico;Centro Universitario Valle de Chalco, Universidad Autónoma del Estado de México, Hermenegildo Galena No. 3, Col. Ma. Isabel, 56615 Valle de Chalco, Mexico;Institute of New Imaging Technologies, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain;Instituto Tecnológico de Toluca, Av. Tecnológico s/n, Ex-Rancho La Virgen, 52140 Metepec, Mexico
Venue:
Pattern Recognition Letters
Year:
2013

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Abstract

Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method.