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Unordered rule discovery using Ant Colony Optimization
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DOI：10.1007/s11432-014-5133-5.png)
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Abstract
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In this article, a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization (ACO). The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets, selected from miscellaneous domains, based on several performance measures. As opposed to its ancestors, our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable. The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.
Keywords:
classification
ant colony optimization
data mining
unordered rule set
comprehensibility
pattern recognition
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