Title: rEDA: reverse estimation of distribution algorithm for classification

Authors: Jian-cong Fan; Zheng Feng; Wen-hua Liu; Yu-hao Cai; Yong-quan Liang

Addresses: State Key Laboratory of Mining Disaster Prevention and Control, Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, 266590, China; College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China ' College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China ' College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China ' College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China ' College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China

Abstract: Estimation of distribution algorithm (EDA) is a branch of evolutionary algorithms. EDA replaces recombination and mutation operators with the estimation of probabilistic distribution of selected individuals. However, these selected individuals only cover part of the problem to be optimised, which causes that the algorithm may easily fall into a local optimum. In this paper, we propose a variation of EDA, reverse estimation of distribution algorithm (rEDA), from the perspective of reverse process. Different from the EDA process that individuals are firstly given and then the estimation of models starts, rEDA is to firstly give initial models and then regulate these models relying on sampling from the models and optimisation objective. We employ rEDA to classification in data mining area and propose a novel classification algorithm based on rEDA. The proposed rEDA algorithm and rEDA-based classification algorithm are analysed theoretically. The empirical results show our proposed algorithm outperforms some classical classification algorithms in accuracy.

Keywords: estimation of distribution algorithm; EDA; reverse process; data mining; classification accuracy; evolutionary algorithms.

DOI: 10.1504/IJICA.2015.072989

International Journal of Innovative Computing and Applications, 2015 Vol.6 No.3/4, pp.137 - 144

Received: 29 Jan 2015
Accepted: 14 May 2015
Published online: 11 Nov 2015 *

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