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• W2033144154 abstract "An ensemble performs well when the component classifiers are diverse yet accurate, so that the failure of one is compensated for by others. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training pattern generation. The method alters input feature values of some patterns using the values of other patterns to generate different patterns for different classifiers. The effectiveness of neural network ensemble based on the proposed technique was evaluated using a suite of 25 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Experimental investigation of different input values alteration techniques finds that alteration with pattern values in the same class is better for generalization, although other alteration techniques may offer more diversity." @default.
• W2033144154 created "2016-06-24" @default.
• W2033144154 creator A5018767527 @default.
• W2033144154 creator A5043721074 @default.
• W2033144154 date "2012-02-01" @default.
• W2033144154 modified "2023-09-23" @default.
• W2033144154 title "ENSEMBLES OF NEURAL NETWORKS BASED ON THE ALTERATION OF INPUT FEATURE VALUES" @default.
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• W2033144154 doi "https://doi.org/10.1142/s0129065712003079" @default.
• W2033144154 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22262526" @default.
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