Mining the data from a hyperheuristic approach using associative classification

Authors:
Fadi Thabtah;Peter Cowling
Affiliations:
Department of Computing and Engineering, University of Huddersfield, Huddersfield, UK;MOSAIC Research Centre, University of Bradford, Bradford, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

Associative classification is a promising classification approach that utilises association rule mining to construct accurate classification models. In this paper, we investigate the potential of associative classifiers as well as other traditional classifiers such as decision trees and rule inducers in solutions (data sets) produced by a general-purpose optimisation heuristic called the hyperheuristic for a personnel scheduling problem. The hyperheuristic requires us to decide which of several simpler search neighbourhoods to apply at each step while constructing a solutions. After experimenting 16 different solution generated by a hyperheuristic called Peckish using different classification approaches, the results indicated that associative classification approach is the most applicable approach to such kind of problems with reference to accuracy. Particularly, associative classification algorithms such as CBA, MCAR and MMAC were able to predict the selection of low-level heuristics from the data sets more accurately than C4.5, RIPPER and PART algorithms, respectively.