Associative classifier for uncertain data

作者：Qin Xiangju; Zhang Yang^*; Li Xue; Wang Yong

来源：11th International Conference on Web-Age Information Management, WAIM 2010, 2010-07-15 to 2010-07-17.

DOI：10.1007/978-3-642-14246-8_66

• Apriori
• Associative Classification
• Associative classifiers
• Classification tasks
• Conventional classifier
• Data sets
• Data uncertainty
• Market analysis
• Medical diagnosis
• Real-world application
• UCI repository
• Uncertain datas

Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. And uncertainty may render many conventional classifiers inapplicable to uncertain classification tasks. In this paper, based on U-Apriori algorothm and CBA algorithm, we propose an associative classifier for uncertain data, uCBA (uncertain Classification Based on Associative), which can classify both certain and uncertain data. The algorithm redefines the support, confidence, rule pruning and classification strategy of CBA. Experimental results on 21 datasets from UCI Repository demonstrate that the proposed algorithm yields good performance and has satisfactory performance even on highly uncertain data.

• 出版日期2010
• 单位university of Queensland; The University of Queensland