Abstract
Associative classification is one of rule-based classifiers that has been applied in many real-world applications. Associative classifier is easily interpretable in terms of classification rules. However, there is room for improvement when associative classification applied for imbalanced classification task. Existing associative classification algorithms can be limited in their performance on highly imbalanced datasets in which the class of interest is the minority class. Our objective is to improve the accuracy of the associative classifier on highly imbalanced datasets. In this paper, an effective cost-sensitive rule ranking method, named (SSCR Statistically Significant Cost-sensitive Rules), is proposed to estimate risks of a rule in classifying unseen data. Risk of a statistically significant association rule is estimated based on its classification cost induced from the training data. SSCR combines statistically significant association rules with cost-sensitive learning to build an associative classifier. Experimental results show that SSCR achieves best performance in terms of true positive rate and recall on real-world imbalanced datasets, compared with CBA and C4.5.
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Waiyamai, K., Suwannarattaphoom, P. (2014). A Cost-Sensitive Based Approach for Improving Associative Classification on Imbalanced Datasets. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_3
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DOI: https://doi.org/10.1007/978-3-319-08979-9_3
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