Classifier systems and genetic algorithms
Artificial Intelligence
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using Model Trees for Classification
Machine Learning
Machine Learning
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Machine Learning
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Constructive meta-learning with machine learning method repositories
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
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In this paper, we present an evaluation of learning algorithms to select proper ones in a rule evaluation support tool for post-processing of mined results. Post-processing of mined results is one of the key processes in the data mining process. However, it is difficult for human experts to completely evaluate several thousand of rules from a large dataset with noises. To reduce the costs in such a rule evaluation task, we have developed a rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance the adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we performed the case study on the meningitis data mining as an actual problem. The obtained results demonstrate the applicability of the proposed rule evaluation support method.