Abstract A feature selection algorithm based on association rules is presented, and the impact of support and confidence on the presented method are studied. The experimental results show that the feature subset size and classification accuracy of the presented method are better than those of other methods. Furthermore, the results indicate high support and confidence levels do not guarantee high classification accuracy and small feature subset, and the sufficient number of rules is the precondition for high efficiency of feature selection based on association rules.
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2009, Vol. 22 
