Stochastic attribute selection committees

Zijian Zheng, Geoffrey I. Webb

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)

Abstract

Classifier committee learning methods generate multiple classifters to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the final classification. Two such methods, Bagging and Boosting, have shown great success with decision tree learning. They create different classifiers by modifying the distribution of the training set. This paper studies a different approach: Stochastic Attribute Selection Committee learning of decision trees. It generates classifier committees by stochastically modifying the set of attributes but keeping the distribution of the training set unchanged. An empirical evaluation of a variant of this method, namely Snsc, in a representative collection of natural domains shows that the SASC method can significantly reduce the error rate of decision tree learning. On average SASC is more accurate than Bagging and less accurate than Boosting, although a one-tailed sign-test fails to show that these differences are significant at a level of 0.05. In addition, it is found that, like Bagging, Snsc is more stable than Boosting in terms of less frequently obtaining significantly higher error rates than C4.5 and, when error is raised, producing lower error rate increases. Moreover, like Bagging, Snsc is amenable to parallel and distributed processing while Boosting is not.

Original languageEnglish
Title of host publicationAdvanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers
EditorsGrigoris Antoniou, John Slaney
PublisherSpringer
Pages321-332
Number of pages12
ISBN (Print)3540651381, 9783540651383
Publication statusPublished - 1 Jan 1998
Externally publishedYes
Event11th Australian Joint Conference on Artificial Intelligence, AI 98 - Brisbane, Australia
Duration: 13 Jul 199817 Jul 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1502
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Australian Joint Conference on Artificial Intelligence, AI 98
CountryAustralia
CityBrisbane
Period13/07/9817/07/98

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