کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
387934 660913 2008 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Mining the data from a hyperheuristic approach using associative classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Mining the data from a hyperheuristic approach using associative classification
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 34, Issue 2, February 2008, Pages 1093–1101
نویسندگان
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