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

被引：56

作者：

Alejo, R. ^{[1
]}

Valdovinos, R. M. ^{[2
]}

Garcia, V. ^{[3
]}

Pacheco-Sanchez, J. H. ^{[4
]}

机构：

[1] Tecnol Estudios Super Jocotitlan, Col Ejido San Juan Yuan 50700, Jocotitlan, Mexico

[2] Univ Autonoma Estado Mexico, Ctr Univ Valle Chalco, Col Ma Isabel 56615, Valle De Chalco, Mexico

[3] Univ Jaume 1, Inst New Imaging Technol, Castellon de La Plana 12071, Spain

[4] Inst Tecnol Toluca, Ex Rancho La Virgen 52140, Metepec, Mexico

来源：

PATTERN RECOGNITION LETTERS | 2013年 / 34卷 / 04期

关键词：

Multi-class imbalance; Overlapping; Back-propagation; Cost function; Editing techniques; CLASSIFICATION; ALGORITHM; SELECTION;

D O I：

10.1016/j.patrec.2012.09.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Class imbalance and class overlap are two of the major problems in data inining 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. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：380 / 388

页数：9

共 33 条

[1] Training radial basis function neural networks: effects of training set size and imbalanced training sets [J].

Al-Haddad, L ;

Morris, CW ;

Boddy, L .

JOURNAL OF MICROBIOLOGICAL METHODS, 2000, 43 (01) :33-44

[2] AN IMPROVED ALGORITHM FOR NEURAL-NETWORK CLASSIFICATION OF IMBALANCED TRAINING SETS [J].

ANAND, R ;

MEHROTRA, KG ;

MOHAN, CK ;

RANKA, S .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1993, 4 (06) :962-969

[3] A review of instance selection methods [J].

Arturo Olvera-Lopez, J. ;

Ariel Carrasco-Ochoa, J. ;

Francisco Martinez-Trinidad, J. ;

Kittler, Josef .

ARTIFICIAL INTELLIGENCE REVIEW, 2010, 34 (02) :133-143

[4]

Asuncion D.N. A., 2007, UCI MACHINE LEARNING

[5]

Batista GEAPA, 2005, LECT NOTES COMPUT SC, V3646, P24

[6] An experimental comparison of classification algorithms for imbalanced credit scoring data sets [J].

Brown, Iain ;

Mues, Christophe .

EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (03) :3446-3453

[7] Classification of imbalanced remote-sensing data by neural networks [J].

Bruzzone, L ;

Serpico, SB .

PATTERN RECOGNITION LETTERS, 1997, 18 (11-13) :1323-1328

[8] SMOTE: Synthetic minority over-sampling technique [J].

Chawla, Nitesh V. ;

Bowyer, Kevin W. ;

Hall, Lawrence O. ;

Kegelmeyer, W. Philip .

2002, American Association for Artificial Intelligence (16)

[9] Nearest neighbour editing and condensing tools-synergy exploitation [J].

Dasarathy, BV ;

Sánchez, JS ;

Townsend, S .

PATTERN ANALYSIS AND APPLICATIONS, 2000, 3 (01) :19-30

[10]

Demsar J, 2006, J MACH LEARN RES, V7, P1

← 1 2 3 4 →