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• W2178748621 abstract "In this paper, we propose two approaches to obtain accurate classifiers for dealing with multi-category classification problem. Our work is based on one-vs-all strategy where we try to decrease conflicting situations. In the first phase of both approaches we employ Genetic Programming to find populations of the best discriminant functions (one population for each class). In addition to traditional function set, like { + , - , * ,/ } , we utilize other special functions in our binary trees. We also use both negative and positive constants in the terminal nodes of the trees. In the second phase, we employ Ant Colony in our first approach, called GP-Ant, and Genetic Algorithm in the second one, called GP-GA, to find the best combination of discriminant functions found in the previous phase. We also provide a special modification box to modify the decision of our final integrated classifiers, when conflicting situations happen. To cope with conflicting situations, we also utilize an appropriate fitness function in the second phase. We compare our works with both state of the art and basic multi-category classification methods on eight well-known publicly available data sets. Our experimental results show that our methods are statistically significantly better than all the other classification methods used. HighlightsWe propose two approaches for designing accurate multi-category classifiers.Our approaches consist of two phases in the training stage.We provide a special modification box to cope with conflicting situations.Several special functions are employed in our binary tree function nodes.We use both negative and positive constants in our binary tree terminal nodes." @default.
• W2178748621 created "2016-06-24" @default.
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• W2178748621 date "2016-01-01" @default.
• W2178748621 modified "2023-09-27" @default.
• W2178748621 title "Designing efficient discriminant functions for multi-category classification using evolutionary methods" @default.
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• W2178748621 doi "https://doi.org/10.1016/j.neucom.2015.08.093" @default.
• W2178748621 hasPublicationYear "2016" @default.
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