Learning acyclic rules based on Chaining Genetic Programming
Refereed conference paper presented and published in conference proceedings

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AbstractMulti-class problem is the class of problems having more than one classes in the data set. Bayesian Network (BN) is a well-known algorithm handling the multi-class problem and is applied to different areas. But BN cannot handle continuous values. In contrast, Genetic Programining (GP) can handle continuous values and produces classification rules. However, GP is possible to produce cyclic rules representing tautologic, in which are useless for inference and expert systems. Co-evolutionary, Rule-chaining Genetic Programming (CRGP) is the first variant of GP handling the multi-class problem and produces acyclic classification rules [16]. It employs backward chaining inference to carry out classification based on the acquired acyclic rule set. It can handle multi-classes; it can avoid cyclic rules; it can handle input attributes with continuous values; and it can learn complex relationships among the attributes. It? this paper, we propose a novel algorithm, the Chaining Genetic Programming (CGP) learning a set of acyclic rules and to produce better results than the CRGP's. The experimental results demonstrate that the proposed algorithm has the shorter learning process and can produce more accurate acyclic classification rules.
All Author(s) ListShum WH, Leung KS, Wong ML
Name of Conference4th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA-06)
Start Date of Conference08/03/2006
End Date of Conference11/03/2006
Place of ConferenceSharjah
Country/Region of ConferenceUnited Arab Emirates
Detailed descriptionorganized by IEEE Computer Society,
Pages959 - 966
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Telecommunications

Last updated on 2021-21-02 at 00:31