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 |
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Title of host publication | 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings |
Pages | 409-414 |
Number of pages | 6 |
DOIs | |
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 |
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Abbreviated title | NABIC 2009 |
Country/Territory | India |
City | Coimbatore |
Period | 9/12/09 → 11/12/09 |
Keywords
- Data mining
- Decision trees
- Evolutionary algorithm
- Hyper-heuristics