Machine Learning
Data preparation for data mining
Data preparation for data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Construct robust rule sets for classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Entropy-based associative classification algorithm for mining manufacturing data
International Journal of Computer Integrated Manufacturing
A granular agent evolutionary algorithm for classification
Applied Soft Computing
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
An integrated framework for optimizing automatic monitoring systems in large IT infrastructures
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper studies a problem of robust rule-based classification, i.e., making predictions in the presence of missing values in data. This study differs from other missing value handling research in that it does not handle missing values but builds a rule-based classification model to tolerate missing values. Based on a commonly used rule-based classification model, we characterize the robustness of a hierarchy of rule sets as k{\hbox{-}}{\rm{optimal}} rule sets with the decreasing size corresponding to the decreasing robustness. We build classifiers based on k{\hbox{-}}{\rm{optimal}} rule sets and show experimentally that they are more robust than some benchmark rule-based classifiers, such as C4.5rules and CBA. We also show that the proposed approach is better than two well-known missing value handling methods for missing values in test data.