Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scoring the Data Using Association Rules
Applied Intelligence
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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DS '99 Proceedings of the Second International Conference on Discovery Science
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ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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Decision Support Systems
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Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Efficient implementation of associative classifiers for document classification
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Adapting the CBA algorithm by means of intensity of implication
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Associative classification has attracted substantial interest in recent years and been shown to yield good results. However, research in this field tends to focus on the development of class classifiers, but the required probability classifier of imbalance data has not been addressed comprehensively. This investigation presents a new associative classification method called Probabilistic Classification based on Association Rules (PCAR). PCAR is based on modifying the rule sorting index, the pruning method, and the scoring procedure in the CBA algorithm. CBA can be generalized to construct a probability classifier. Additionally, it can improve the efficiency of associative classification for predicting imbalance data. Experiments that use both benchmarking datasets and real-life application datasets reveal that the new method outperforms the previous associative classification algorithm and C5.0 for all datasets. Also, in some datasets, the predictive performance exceeds that achieved by logistic regression and the use of a neural network.