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Content Provider | IEEE Xplore Digital Library |
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Author | Xi-Zhao Wang Hui-Min Feng |
Copyright Year | 2004 |
Description | Author affiliation: Machine Learning Center, Hebei Univ., Baoding, China (Xi-Zhao Wang; Hui-Min Feng) |
Abstract | Fuzzy integral is a valid method for combining multiple classifiers. However in the fusion based on fuzzy integral, how to choose an appropriate fuzzy measure is a difficult but important problem. The system's performance is largely dependent of the fuzzy measure. An appropriate fuzzy measure can make the system's performance better than the best individual classifier, while an inappropriate fuzzy measure will result in worse performance than the individual classifiers. This paper investigates the fusion mechanism based on the fuzzy integral for multiple classifiers, and discusses the impact of fuzzy measures or nonnegative set functions on the fusion. The study is useful to obtain an appropriate fuzzy measure for improving the performance of the system. |
Starting Page | 2020 |
Ending Page | 2023 |
File Size | 221026 |
Page Count | 4 |
File Format | |
ISBN | 0780384032 |
DOI | 10.1109/ICMLC.2004.1382126 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2004-08-26 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Fuzzy sets Fuzzy systems Neural networks Density measurement Power measurement Machine learning Educational institutions Mathematics Computer science Robustness |
Content Type | Text |
Resource Type | Article |
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