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Attribute Reduction Function Mining Algorithm Based on Gene Expression Programming

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5 Author(s)
Chang-an Yuan ; Coll. of Comput., Sichuan Univ., Chengdu ; Chang-jie Tang ; Jie Zuo ; An-Long Chen
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When mining non-linearity function with large number of variables, traditional methods cannot effectively reduce the conditional attributes. To solve the problem, this paper proposes GEP-ARFM model. The model includes the concepts of marginal gene, marginal fitness, and revised fitness and the algorithms of GEPAMF, GARFM-GEP, and SARFM-GEP. The comparison experiments show that (1) both GARFM-GEP and SARFM-GEP can effectively reduce the conditional attributes to find the best function expression. (2) The precision of function expression by using SARFM-GEP is approximate with using GARFM-GEP algorithm. (3) SARFM-GEP method is 300 times faster than GARFM-GEP in the case of 20 independent variables. (4) The fitness value of the function expression got by using GEP-ARFM model is 24.6% greater than the traditional method

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 2006