The Effect of Evolved Attributes on Classification Algorithms

M. A. Muharram, G. D. Smith

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)

Abstract

We carry out a systematic study of the effect on the performance of a range of classification algorithms with the inclusion of attributes constructed using genetic programming. The genetic program uses information gain as the basis of its fitness. The classification algorithms used are C5, CART, CHAID and a MLP. The results show that, for the majority of the data sets used, all algorithms benefit by the inclusion of the evolved attributes. However, for one data set, whilst the performance of C5 improves, the performance of the other techniques deteriorates. Whilst this is not statistically significant, it does indicate that care must be taken when a pre-processing technique (attribute construction using GP) and the classification technique (in this case, C5) use the same fundamental technology, in this case Information Gain.
Original languageEnglish
Title of host publicationAI 2003: Advances in Artificial Intelligence
EditorsTamás Domonkos Gedeon, Lance ChunChe Fung
PublisherSpringer Berlin / Heidelberg
Pages933-941
Number of pages9
Volume2903
ISBN (Print)978-3-540-20646-0
DOIs
Publication statusPublished - 2003
Event16th Australian Conference on AI - Perth, Australia
Duration: 3 Dec 20035 Dec 2003

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

Conference

Conference16th Australian Conference on AI
CountryAustralia
CityPerth
Period3/12/035/12/03

Cite this