NDLI: Finding Compact Classification Rules With Parsimonious Gene Expression Programming

Content Provider	IEEE Xplore Digital Library
Author	Weihong Wang Qu Li Zhihua Cai
Copyright Year	2005
Description	Author affiliation: Coll. of Software, Zhejiang Univ. of Technol., Hangzhou (Weihong Wang)
Abstract	Gene expression programming (GEP) is a new evolutionary computation technique. Its fixed-length representation spares it from the problem of bloat, which is a serious problem of GP. Recent work has shown that GEP can discover accurate and understandable classification rules in the form of mathematical expressions. But some of the rules are still too large than necessary because of the existence of useless parts in genes. Basic GEP classifiers don't have the ability to remove the useless parts from the classification rules. This paper presents a new fitness function incorporating a parsimonious term to evolve more compact rules with parsimonious gene expression programming (PGEP). Results show that PGEP can find more parsimonious rules without losing accuracy
Starting Page	702
Ending Page	705
File Size	813741
Page Count	4
File Format	PDF
ISBN	0780394224
DOI	10.1109/ICNNB.2005.1614725
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Geology Genetic programming Evolutionary computation Educational institutions Electronic mail Gene expression Functional programming Decision trees Biological cells
Content Type	Text
Resource Type	Article

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