Feature selection plays an important role in data mining and pattern recognition, especially in the case of large scale\r\ndata. Feature selection is done due to large amount of noise and irrelevant features in the original data set. Hence,\r\nthe efficiency of learning algorithms will increase incredibly if these irrelevant data are removed by this procedure.\r\nA novel approach for feature selection is introduced in this paper using CHABCF, (Chaotic Artificial Bee Colony\r\nbased on Fuzzy), algorithm which is a combination of three paradigms: (1) Chaos theory (2) Artificial Bee Colony\r\noptimization and (3) Fuzzy logic. The fuzzy logic is used for ambiguity removal while chaos is used for generating\r\nbetter diversity in the initial population of our bee colony optimization algorithm. To demonstrate the efficiency of\r\nour algorithm, we have tested it on some well-known benchmarks such as wine, diabet and iris
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