A Binary Superior Tracking Artificial Bee Colony for Feature Selection
作者： Xianghua Chu , Shuxiang Li , Wenjia Mao , Wei Zhao , Linya Huang
DOI: 10.1007/978-981-15-7670-6_25
关键词: Tracking (particle physics) 、 Artificial intelligence 、 Capacity enhancement 、 Particle swarm optimization 、 Computer science 、 Binary number 、 Dimension (vector space) 、 Pattern recognition 、 Feature selection 、 Set (abstract data type)
摘要: Feature selection is a NP-hard combinatorial problem of selecting the effective features from given set original to reduce dimension dataset. This paper aims propose an improved variant learning algorithm for feature selection, termed as Binary Superior Tracking Artificial Bee Colony (BST-ABC) algorithm. In BST-ABC, binary strategy proposed enable each bee learn superior individuals in exploitation capacity enhancement. Ten datasets UCI repository are adopted test problems, and results BST-ABC compared with particle swarm optimization (PSO) ABC. Experimental demonstrate that could obtain optimal classification accuracy minimum number features.
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sci-hub.st 参考文章(13)
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