Title: A novel classification learning framework based on estimation of distribution algorithms

Authors: Jiancong Fan; Qiang Xu; Yongquan Liang

Addresses: College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong Province, China ' College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong Province, China ' College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong Province, China

Abstract: Estimation of distribution algorithms (abbr. EDAs) is a relatively new branch of evolutionary algorithms. EDAs replace search operators with the estimation of the distribution of selected individuals + sampling from the population. In an EDAs, this explicit representation of the population is replaced with a probability distribution over the choices available at each position in the vector that represents a population member. In this paper, an estimation of distribution learning framework and the corresponding learning algorithm are proposed and the relevant properties of the framework are analysed on the basis of probability. The framework provides a basis and a principle criterion for designing and analysing evolutionary learning algorithms based on EDAs. The probability is the core tool of EDAs. EDA-based learning algorithms are required to estimate the population distribution by the sample distributions. The learning framework proposed can guide and regulate the design processes of learning algorithms and strategies based on EDAs. The framework involved in relevant learning problems is analysed from the perspectives of probability by properties analysis, proof and verification. The experiment results show that the framework proposed is feasible for realising learning from datasets and has better learning performances than some other relevant evolutionary learning methods.

Keywords: evolutionary computation; classification learning; estimation of distribution algorithms; EDAs; machine learning.

DOI: 10.1504/IJCSM.2012.051620

International Journal of Computing Science and Mathematics, 2012 Vol.3 No.4, pp.353 - 366

Available online: 23 Jan 2013 *

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