Feature Selection Based on Improved Runner-Root Algorithm Using Chaotic Singer Map and Opposition-Based Learning
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.
KeywordsFeature selection (FS) Opposition-based learning (OBL) Metaheuristic algorithms (MH) Chaotic map Runner-Root Algorithm (RRA) Swarm intelligence (SI)
This work are supported by the Natural Science Foundation of Hubei Province of China under Grant No. 2016CFB541 and the Applied Basic Research Program of Wuhan Science and Technology Bureau of China under Grant No. 2016010101010003 and the Science and Technology Program of Shenzhen of China under Grant No. JCYJ20170307160458368.
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