Abstract
The Associative Classification approach combining the classification and association rule mining techniques is now becoming reliable classification strategy for obtaining moderate sized classifiers with enhanced accuracy rate. The associative classification techniques when applied on large databases produce huge number of rules and make difficult to achieve effective classification model. To overcome the problems underlying the associative classification, this paper focuses on rule ranking criteria used during rule generation process and proposes the unique rule pruning procedures followed by discussing their impact on classification accuracy rate. The goal of the study is to effectively apply the rules produced classify the data and to obtain reduced size classifiers with high classification accuracy rate.
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Mittal, K. An approach towards enhancement of classification accuracy rate using efficient pruning methods with associative classifiers. Int. j. inf. tecnol. 14, 1525–1533 (2022). https://doi.org/10.1007/s41870-021-00673-3
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DOI: https://doi.org/10.1007/s41870-021-00673-3