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• W2161631032 abstract "We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier system. The first GA version selects disjoint feature subsets to be used by the individual classifiers, whereas the second version selects (possibly) overlapping feature subsets, and also the types of the individual classifiers. The two GAs have been tested with four real data sets: heart, Satimage, letters, and forensic glasses. We used three-classifier systems and basic types of individual classifiers (the linear and quadratic discriminant classifiers and the logistic classifier). The multiple-classifier systems designed with the two GAs were compared against classifiers using: all features; the best feature subset found by the sequential backward selection method; and the best feature subset found by a CA. The GA design can be made less prone to overtraining by including penalty terms in the fitness function accounting for the number of features used." @default.
• W2161631032 created "2016-06-24" @default.
• W2161631032 creator A5039168231 @default.
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• W2161631032 date "2000-01-01" @default.
• W2161631032 modified "2023-09-23" @default.
• W2161631032 title "Designing classifier fusion systems by genetic algorithms" @default.
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• W2161631032 doi "https://doi.org/10.1109/4235.887233" @default.
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