Hyper-heuristic decision tree induction

Alan Vella; David Corne; Chris Murphy

doi:10.1109/NABIC.2009.5393568

Hyper-heuristic decision tree induction

Alan Vella, David Corne, Chris Murphy

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings
Pages	409-414
Number of pages	6
DOIs	https://doi.org/10.1109/NABIC.2009.5393568
Publication status	Published - 2009
Event	2009 World Congress on Nature and Biologically Inspired Computing - Coimbatore, India Duration: 9 Dec 2009 → 11 Dec 2009

Conference

Conference	2009 World Congress on Nature and Biologically Inspired Computing
Abbreviated title	NABIC 2009
Country	India
City	Coimbatore
Period	9/12/09 → 11/12/09

Keywords

Data mining
Decision trees
Evolutionary algorithm
Hyper-heuristics

Access to Document

10.1109/NABIC.2009.5393568

Link to publication in Scopus

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Cite this

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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. {\textcopyright}2009 IEEE.",

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AU - Murphy, Chris

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AB - 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.

KW - Data mining

KW - Decision trees

KW - Evolutionary algorithm

KW - Hyper-heuristics

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