Hyper-heuristic decision tree induction

Alan Vella, David Corne, Chris Murphy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyper-heuristics have been little explored in data mining. Here we apply a hyper-heuristic approach to data mining, by searching a space of decision tree induction algorithms. The result of hyper-heuristic search in this case is a new decision tree induction algorithm. We show that hyperheuristic search over a space of decision tree induction algorithms can find decision tree induction algorithms that outperform many different version of ID3 on unseen test sets. ©2009 IEEE.

Original languageEnglish
Title of host publication2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings
Pages409-414
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 World Congress on Nature and Biologically Inspired Computing - Coimbatore, India
Duration: 9 Dec 200911 Dec 2009

Conference

Conference2009 World Congress on Nature and Biologically Inspired Computing
Abbreviated titleNABIC 2009
CountryIndia
CityCoimbatore
Period9/12/0911/12/09

Keywords

  • Data mining
  • Decision trees
  • Evolutionary algorithm
  • Hyper-heuristics

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