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Proceedings Paper

New algorithm for combining classifiers based on fuzzy integral and genetic algorithms
Author(s): Yurong Li; Jingping Jiang
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Paper Abstract

Combination of many different classifiers can improve classification accuracy. Sugeno and choquet integrals with respect to the fuzzy measure possess many desired properties, so in this paper they are used to combine multiple neural network classifiers. However, it is difficult to determine fuzzy measures in real problems. In this paper, we present two methods, one is that we assign the degree of importance of each network based on how good these networks classify each class of the training data, the other is by genetic algorithms (GAs), to obtain fuzzy measures, each taking into account the intuitive idea that each classifier always possesses different classification ability for each class. In the experiment, several databases in UCI repository are tested using these combination schemes and compared with C4.5. They are also applied to a multisensor fusion system for workpiece identification. Experimental results confirm the superiority of these presented methods.

Paper Details

Date Published: 24 September 2001
PDF: 6 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441640
Show Author Affiliations
Yurong Li, Zhejiang Univ. and Fuzhou Univ. (China)
Jingping Jiang, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies
Jun Shen; Sharatchandra Pankanti; Runsheng Wang, Editor(s)

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