Abstract
We propose a novel approach which extracts consistent (100% confident) rules and builds a classifier with them. Recently, associative classifiers which utilize association rules have been widely studied. Indeed, the associative classifiers often outperform the traditional classifiers. In this case, it is important to collect high quality (association) rules. Many algorithms find only high support rules, because decreasing the minimum support to be satisfied is computationally demanding. However, it may be effective to collect low support but high confidence rules. Therefore, we propose an algorithm that produces a wide variety of 100% confident rules including low support rules. To achieve this goal, we adopt a specific-to-general rule searching strategy, in contrast to the previous many approaches. Our experimental results show that the proposed method achieves higher accuracies in several datasets taken from UCI machine learning repository.
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Shidara, Y., Nakamura, A., Kudo, M. (2007). CCIC: Consistent Common Itemsets Classifier. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_37
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DOI: https://doi.org/10.1007/978-3-540-73499-4_37
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