期刊
NEURAL INFORMATION PROCESSING, ICONIP 2017, PT V
卷 10638, 期 -, 页码 156-166出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-70139-4_16
关键词
Feature selection (FS); Opposition-based learning (OBL); Metaheuristic algorithms (MH); Chaotic map; Runner-Root Algorithm (RRA); Swarm intelligence (SI)
资金
- Natural Science Foundation of Hubei Province of China [2016CFB541]
- Applied Basic Research Program of Wuhan Science and Technology Bureau of China [2016010101010003]
- Science and Technology Program of Shenzhen of China [JCYJ20170307160458368]
The feature selection (FS) is an important step for data analysis. FS is used to reduce the dimension of data by selecting the relevant features; while removing the redundant, noisy and irrelevant features that lead to degradation of the performance. Several swarm techniques are used to solve the FS problem and these methods provide results better than classical approaches. However, most of these techniques have limitations such as slow convergence and time complexity. These limitations occur due that all the agents update their position according to the best one. However, this best agent may be not the optimal global solution for FS, therefore, the swarm getting stuck in a local solution. This paper proposes an improved Runner-Root Algorithm (RRA). The RRA is combined with chaotic Singer map and opposition-based learning to increase its accuracy. The experiments are performed in eight datasets and the performance of the proposed method is compared against swarm algorithms.
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