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• W3177035345 abstract "In the multi-class classification problem GP plays an important role when combined with other non-GP classifiers. However, when GP performs the actual classification (without relying on other classifiers) its classification accuracy is low. This is especially true when the number of classes is high. In this paper, we present DTC, a GP classifier that leverages the effectiveness of the dynamic target approach to evolve a set of discriminant functions (one for each class). Notably, DTC is the first GP classifier that defines the fitness of individuals by using the synergistic combination of linear scaling and the hinge-loss function (commonly used by SVM). Differently, most previous GP classifiers use the number of correct classifications to drive the evolution. We compare DTC with eight state-of-art multi-class classification techniques (e.g., RF, RS, MLP, and SVM) on eight popular datasets. The results show that DTC achieves competitive classification accuracy even with 15 classes, without relying on other classifiers." @default.
• W3177035345 created "2021-07-05" @default.
• W3177035345 creator A5011541744 @default.
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• W3177035345 date "2021-06-26" @default.
• W3177035345 modified "2023-09-26" @default.
• W3177035345 title "Towards effective GP multi-class classification based on dynamic targets" @default.
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• W3177035345 doi "https://doi.org/10.1145/3449639.3459324" @default.
• W3177035345 hasPublicationYear "2021" @default.
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