C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
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
Information Retrieval
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Metric for Selection of the Most Promising Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Machine Learning of Credible Classifications
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in 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
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
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
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
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Improving a rule evaluation support method based on objective indices
International Journal of Advanced Intelligence Paradigms
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In datamining post-processing, rule selection with objective rule evaluation indices is one of useful methods for extracting valuable knowledge from mined patterns. However, the relationship between an index value and experts' criteria has never been clarified. In order to determine the relationship, we have developed a method to obtain learning models from a dataset consisting of objective rule evaluation indices and evaluation labels for rules. In this study, we have compared accuracies of classification learning algorithms for datasets with randomized class labels. Then, the result shows that accuracies of classification learning algorithms without any criterion of a human expert can not outperform each percentage of majority class on both of the balanced and imbalanced class distribution datasets. With regarding to this result, we can determine whether or not a labeled rule set contains some criteria based on the dataset consisting the objective rule evaluation indices.