Attribute Selection Method Based on Improved Discrete Glowworm Swarm Optimization and Fractal Dimension
NI Zhi-Wei, XIAO Hong-Wang, WU Zhang-Jun, XUE Yong-Jian
School of Management, Hefei University of Technology, Hefei 230009 Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009
Abstract：Attribute selection is an important method of data preprocessing in the field of data mining. An improved attribute selection method is proposed which combines discrete glowworm swarm optimization (DGSO) algorithm with fractal dimension. In this method,fractal dimension is taken as the evaluation criteria for attribute subsets and DGSO algorithm as a kind of search strategy. To analyze the feasibility and the effectiveness of the proposed method,six UCI datasets are used in the experiments,and the 10-fold cross validation and support vector machine algorithm are utilized to evaluate the classification accuracy before and after attribute selection. Then, different evaluation criteria and search strategies are compared and the parameters are analyzed in detail. The experimental results show that the proposed method has comparatively high feasibility and effectiveness.
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2013, Vol. 26 
