Convex hulls in concept induction

D. A. Newlands, G. I. Webb

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

Abstract

This paper investigates modelling concepts as a few, large convex hulls rather than as many, small, axis-orthogonal divisions as is done by systems which currently dominate classification learning. It is argued that this approach produces classifiers which have less strong hypothesis language bias and which, because of the fewness of the concepts induced, are more understandable. The design of such a system is described and its performance is investigated. Convex hulls are shown to be a useful inductive generalisation technique offering rather different biases than well-known systems such as C4.5 and CN2. The types of domains where convex hulls can be usefully employed are described.

Original languageEnglish
Title of host publicationMethodologies for Knowledge Discovery and Data Mining - 3rd Pacific-Asia Conference, PAKDD 1999, Proceedings
EditorsLizhu Zhou, Ning Zhong
PublisherSpringer
Pages306-316
Number of pages11
ISBN (Print)3540658661, 9783540658665
Publication statusPublished - 1 Jan 1999
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 1999 - Beijing, China
Duration: 26 Apr 199928 Apr 1999
Conference number: 3rd
https://link.springer.com/book/10.1007/3-540-48912-6 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1574
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 1999
Abbreviated titlePAKDD 1999
Country/TerritoryChina
CityBeijing
Period26/04/9928/04/99
Internet address

Keywords

  • Classification learning
  • Convex hulls
  • Induction

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