Improving the performance of the BioHEL learning classifier system

Impact Factor:1.965

DOI number:10.1016/j.eswa.2013.05.025

Affiliation of Author(s):Univ Nottingham

Teaching and Research Group:Sch Comp Sci & IT, Nottingham NG8 1BB

Journal:Expert Systems With Applications

Key Words:Learning Classifier Systems,Bioinformatics-oriented Hierarchical Evolutionary Learning,Estimation of Distribution Algorithms

Abstract:The identification of significant attributes is of major importance to the performance of a variety of Learning Classifier Systems including the newly-emerged Bioinformatics-oriented Hierarchical Evolutionary Learning (BioHEL) algorithm. However, the BioHEL fails to deliver on a set of synthetic datasets which are the checkerboard data mixed with Gaussian noises due to the fact the significant attributes were not successfully recognised. To address this issue, a univariate Estimation of Distribution Algorithm (EDA) technique is introduced to BioHEL which primarily builds a probabilistic model upon the outcome of the generalization and specialization operations. The probabilistic model which estimates the significance of each attribute provides guidance for the exploration of the problem space. Experiment evaluations showed that the proposed BioHEL systems achieved comparable performance to the conventional one on a number of real-world small-scale datasets. Research efforts were also made on finding the optimal parameter for the traditional and proposed BioHEL systems. (C) 2013 Elsevier Ltd. All rights reserved.

Co-author:Xiaolei Xia,Huanlai Xing

Document Code:10.1016/j.eswa.2013.05.025

Volume:40

Issue:15

Page Number:6019-6032

ISSN No.:0957-4174

Translation or Not:no

Date of Publication:2013-08-21

Included Journals:SCI