Evaluation of a Classification Rule Mining Algorithm Based on Secondary Differences

Authors:
Shusaku Tsumoto;Hidenao Abe
Affiliations:
Department of Medical Informatics, Shimane University, School of Medicine, Shimane, Japan 693-8501;Department of Medical Informatics, Shimane University, School of Medicine, Shimane, Japan 693-8501
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Year:
2009

Citing 10
Cited 0

Classifier systems and genetic algorithms

Artificial Intelligence
The induction of probabilistic rule sets—the Itrule algorithm

Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Separate-and-Conquer Rule Learning

Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Classification Rule Learning with APRIORI-C

EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Generating Accurate Rule Sets Without Global Optimization

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ROC `n' Rule Learning—Towards a Better Understanding of Covering Algorithms

Machine Learning
Analyzing Behavior of Objective Rule Evaluation Indices Based on a Correlation Coefficient

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rule mining is considered as one of the usable mining method in order to obtain valuable knowledge from stored data on database systems. Although many rule mining algorithms have been developed, almost current rule mining algorithms only use primary difference of a criterion to select attribute-value pairs to obtain a rule set to a given dataset. In this paper, we introduce a rule generation method based on secondary differences of two criteria for avoiding the trade-off of coverage and accuracy. Then, we performed an evaluation of the proposed algorithm by using UCI common datasets. In this case study, we compared the predictive accuracies of rule sets learned by our algorithm with that of three representative algorithms. The result shows that our rule mining algorithm can obtain not only accurate rules but also rules with the other features.