Classification based on specific rules and inexact coverage

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• 作者：Raudel Herná ; ndez-Leó ; n^a ; ^{rhernandez@cenatav.co.cu} ; ^{raudel@ccc.inaoep.mx} ; Jesú ; s A. Carrasco-Ochoa^b ; ^{ariel@inaoep.mx} ; José ; Fco. Martí ; nez-Trinidad^b ; ^{fmartine@inaoep.mx} ; José ; Herná ; ndez-Palancar^a ; ^{jpalancar@cenatav.co.cu}
• 关键词：Data mining ; Supervised classification ; Class association rules ; Association rule mining
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：15 September, 2012
• 卷：39
• 期：12
• 页码：11203-11211
• 文件大小：309 K

摘要

Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature.