Paper Info
Title
Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers.
Abstract
A novel method based on cost-sensitive neural networks with binarization techniques for multi-class problems is developed.The effect of aggregation methods for the proposed method is studied.The positive synergy between the management of non-competent classifiers and the proposed method is found.The effectiveness of our method tested on three different kinds of cost matrices is investigated.In this study, 25 real-world applications, from KEEL dataset repository, are selected for the experimental study. Multi-class classification problems can be addressed by using decomposition strategy. One of the most popular decomposition techniques is the One-vs-One (OVO) strategy, which consists of dividing multi-class classification problems into as many as possible pairs of easier-to-solve binary sub-problems. To discuss the presence of classes with different cost, in this paper, we examine the behavior of an ensemble of Cost-Sensitive Back-Propagation Neural Networks (CSBPNN) with OVO binarization techniques for multi-class problems. To implement this, the original multi-class cost-sensitive problem is decomposed into as many sub-problems as possible pairs of classes and each sub-problem is learnt in an independent manner using CSBPNN. Then a combination method is used to aggregate the binary cost-sensitive classifiers. To verify the synergy of the binarization technique and CSBPNN for multi-class cost-sensitive problems, we carry out a thorough experimental study. Specifically, we first develop the study to check the effectiveness of the OVO strategy for multi-class cost-sensitive learning problems. Then, we develop a comparison of several well-known aggregation strategies in our scenario. Finally, we explore whether further improvement can be achieved by using the management of non-competent classifiers. The experimental study is performed with three types of cost matrices and proper statistical analysis is employed to extract the meaningful findings.
Year
DOI
Venue
2017
10.1016/j.asoc.2017.03.016
Appl. Soft Comput.
Keywords
Field
DocType
Cost-sensitive learning,Neural networks,One-vs-one,Aggregation strategies,Dynamic classifier selection
Data mining,Division (mathematics),Computer science,Matrix (mathematics),Back propagation neural network,Artificial intelligence,Artificial neural network,Machine learning,Statistical analysis,Binary number
Journal
Volume
Issue
ISSN
56
C
1568-4946
Citations 
PageRank 
References 
5
0.40
32
Authors
4
Name
Order
Citations
PageRank
Zhongliang Zhang1362.86
Xing-Gang Luo213814.85
Salvador García34151118.45
Francisco Herrera4273911168.49