一种基于证据距离的多分类器差异性度量

ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2012, Vol. ›› Issue (6): 1093-1099.

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A Novel Diversity Measure of Multiple Classifier Systems Based on Distance of Evidence

YANG Yi¹, HAN Deqiang², HAN Chongzhao²

1. School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, China;
2. Institute of Integrated Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Received:2011-08-31 Revised:2011-10-24 Online:2012-06-25 Published:2012-06-26
Supported by:
National Basic Research Program of China (2007CB311006); National Natural Science Foundation of China (61104214, 61074176, 67114022); China Postdoctoral Science Foundation (20100481337, 201104670); Research Fund of Shanxi Key Laboratory of Electronic Information System Integration (201101Y17); Chongqing Natural Science Foundation(CSCT, 2010BA2003)

Abstract

Abstract: Multiple classifier systems can effectively improve the classification performance in many applications, which is why they have attracted a great deal of interest. Diversity among member classifiers is a necessary condition for improvement in classifier ensemble performance. In this paper, a novel diversity measure of multiple classifier systems is proposed based on the distance of evidence and a new approach to multiple classifier system design is presented. By using jointly the proposed diversity measure, the traditional diversity measure and the classification performance on training samples, an effective multiple classifier system can be implemented. It is experimentally shown that the proposed diversity measure and the proposed approach to multiple classifier system design are rational and effective.

Key words: multiple classifier system, diversity measure, evidence theory, distance of evidence, multiple classifier fusion, classifiers

CLC Number:

V448
TP391.4

YANG Yi, HAN Deqiang, HAN Chongzhao. A Novel Diversity Measure of Multiple Classifier Systems Based on Distance of Evidence[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (6): 1093-1099.

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A Novel Diversity Measure of Multiple Classifier Systems Based on Distance of Evidence

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