APACS: a system for the automatic analysis and classification of conceptual patterns
Computational Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
High-Order Pattern Discovery from Discrete-Valued Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Concept acquisition through representational adjustment
Concept acquisition through representational adjustment
Boosting an Associative Classifier
IEEE Transactions on Knowledge and Data Engineering
A decision support framework for clinical needle EMG
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Expert Systems with Applications: An International Journal
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
CCIC: Consistent Common Itemsets Classifier
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Pattern discovery for large mixed-mode database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Empirical study on weighted voting multiple classifiers
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Parameter inference of cost-sensitive boosting algorithms
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is still seldom found in current publications. Trying to bridge this gap, this paper extends our previous paper on significant event association discovery to classification. We propose to use weight of evidence to evaluate the evidence of a significant event association in support of, or against, a certain class membership. Traditional weight of evidence in information theory is extended here to measure the event associations of different orders with respect to a certain class. After the discovery of significant event associations inherent in a data set, it is easy and efficient to apply the weight of evidence measure for classifying an observation according to any attribute. With this approach, we achieve flexible prediction.