NDLI: An Evolutionary Approach for Dynamic Configuration of Multi-expert Classification Systems

Content Provider	IEEE Xplore Digital Library
Author	De Stefano, C. Delia Cioppa, A. Marcelli, A.
Copyright Year	2006
Description	Author affiliation: Univ. di Cassino, Cassino (De Stefano, C.)
Abstract	We introduce a multiple classifier system that incorporates a global optimization technique based on a Breeder Genetic Algorithm for dynamically selecting the set of experts to be included in the pool. The proposed technique is applicable when the experts provide both the class assigned to the input sample and a measure of the reliability of the classification. For each sample, the experts selected for participating in the voting rule are those whose reliability is larger than a given threshold. There are as many thresholds as the number of experts by the number of classes. The values of the thresholds aimed at selecting the best set of experts for each input sample are determined by the Breeder Genetic Algorithm. The reliability measures provided by the experts of the pool are also used to implement the tie-break mechanism needed within the voting scheme. The system has been tested on the Image database from the UCI database repository by using as classifiers an ensemble of Back-Propagation neural network and an ensemble of Learning Vector Quantization neural network. The voting schemes adopted are the Majority Vote, the Weighted Majority Vote and the Borda Count. The performance of the system is compared with those exhibited by the multi-expert systems exploiting the same combining rules without the dynamic selection of the expert.
Starting Page	2444
Ending Page	2450
File Size	237643
Page Count	7
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688612
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Voting Genetic algorithms Image databases Neural networks Costs System testing Vector quantization Diversity reception Pattern recognition
Content Type	Text
Resource Type	Article

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