NDLI: New Fitness Function for Ranking First-order Rule Based on Binding

Content Provider	IEEE Xplore Digital Library
Author	Zhongwei Xu Feng Liu
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Shanghai Maritime Univ. (Zhongwei Xu; Feng Liu)
Abstract	In most problems of first-order rule learning, rule space is usually structured by thetas-subsumption operator. But in first-order rule space, thetas-subsumption is a quasi-ordering. If the number of coved training examples is used as the criterion for ranking candidates of hypothesis, there is a equivalent-class problem when searching along the quasi-ordering. Rules in an equivalent-class can't be distinguished according to their fitness function values. In another aspect, it makes the search to prefer longer rules, and would reduce the system efficiency and readability of learned rules. To solve these problems, in this paper, a new fitness function based on binding is presented. The contrast experiment has been done to show the effect of the new fitness function in guiding the search through first-order rule space
Starting Page	837
Ending Page	840
File Size	4551077
Page Count	4
File Format	PDF
ISBN	1424403251
DOI	10.1109/SPCA.2006.297541
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Pervasive computing Computer science Fitness Function Training data Optimization methods Robustness First-order rules Learning Information Gain Application software Quasi-ordering Genetic algorithms
Content Type	Text
Resource Type	Article

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