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Improving the performance of the BioHEL learning classifier system

EXPERT SYSTEMS WITH APPLICATIONS 2013年第15期40卷 6019-6032页

作者： Xia, Xiao-Lei Xing, Huanlai Jiaxing Univ Sch Mech & Elect Engn Jiaxing 314001 Peoples R China Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

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.

关键词： learning Classifier Systems bioinformatics-oriented hierarchical evolutionary learning Estimation of Distribution Algorithms

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