Analysis of online signature based learning classifier systems for noisy environments: A feedback control theoretic approach

被引：0

作者：

Shafi, Kamran ^{[1
]}

Abbass, Hussein A. ^{[1
]}

机构：

[1] School of Engineering and Information Technology, University of New South Wales, Canberra

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8886卷

关键词：

Adaptive control; LCS; Learning classifier systems; Noise; Online rule reduction; Signature based LCS; UCS;

D O I：

10.1007/978-3-319-13563-2_34

中图分类号：

学科分类号：

摘要：

Post training rule set pruning techniques are amongst one of the approaches to improve model comprehensibility in learning classifier systems which commonly suffer from population bloating in real-valued classification tasks. In an earlier work we introduced the term signatures for accurate and maximally general rules evolved by the learning classifier systems. A framework for online extraction of signatures using a supervised classifier system was presented that allowed identification and retrieval of signatures adaptively as soon as they are discovered. This paper focuses on the analysis of theoretical bounds for learning signatures using existing theory and the performance of the proposed algorithm in noisy environments using benchmark synthetic data sets. The empirical results with the noisy data show that the mechanisms introduced to adapt system parameters enable signature extraction algorithm to cope with significant levels of noise. © Springer International Publishing Switzerland 2014.

引用

页码：395 / 406

页数：11

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