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
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Using Information-Theoretic Measures to Assess Association Rule Interestingness
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indexes
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Constructive meta-learning with machine learning method repositories
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Investigating Accuracies of Classifications for Randomized Imbalanced Class Distributions
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
A data analysis approach for evaluating the behavior of interestingness measures
DS'05 Proceedings of the 8th international conference on Discovery Science
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It is a key for the successes of data mining projects in practical situations to evaluate the obtained so many patterns as valuable knowledge effectively. In order to provide an effective support, we have been developing a rule evaluation support method based on the learning models of objective rule evaluation indices. In this paper, we report two improvements of this method and their evaluations. One is improved the learning algorithm selection in the proposed method by introducing a constructive meta-learning scheme. The other is improved the sorting efficiency of objective rule evaluation indices by combining them.