Improve class prediction performance using a hybrid data mining approach

Li Fei Chen

doi:10.1109/ICMLC.2009.5212497

Improve class prediction performance using a hybrid data mining approach

Li Fei Chen^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Rough set theory (RST), support vector machine (SVM), and decision tree (DT) are brightly data mining methodologies for classification prediction tasks. While the accuracy for class prediction is highly emphasized, the ability to generate rules for decision support is also important in some practical applications. Studies have shown the ability of RST for feature selection while SVM and DT are significantly on their predictive power. Moreover, the ability of DT for rule generation is an attractive function. This study intents to integrate the advantages of RST, SVM and DT approaches to develop a hybrid data mining approach to improve the performance of class prediction as well as rule generation.

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Publisher	IEEE Computer Society
Pages	210-214
Number of pages	5
ISBN (Print)	9781424437030
DOIs	https://doi.org/10.1109/ICMLC.2009.5212497
Publication status	Published - 2009
Externally published	Yes
Event	8th International Conference on Machine Learning and Cybernetics, ICMLC 2009 - Baoding, China Duration: 2009 Jul 12 → 2009 Jul 15

Publication series

Name	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume	1

Conference

Conference	8th International Conference on Machine Learning and Cybernetics, ICMLC 2009
Country/Territory	China
City	Baoding
Period	2009/07/12 → 2009/07/15

Keywords

Classification
Data mining
Decision trees
Rough set theory
Rule generation
Support vector machine

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Software
Control and Systems Engineering

Access to Document

10.1109/ICMLC.2009.5212497

Cite this

Chen, L. F. (2009). Improve class prediction performance using a hybrid data mining approach. In Proceedings of the 2009 International Conference on Machine Learning and Cybernetics (pp. 210-214). Article 5212497 (Proceedings of the 2009 International Conference on Machine Learning and Cybernetics; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2009.5212497

Improve class prediction performance using a hybrid data mining approach. / Chen, Li Fei.
Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. IEEE Computer Society, 2009. p. 210-214 5212497 (Proceedings of the 2009 International Conference on Machine Learning and Cybernetics; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, LF 2009, Improve class prediction performance using a hybrid data mining approach. in Proceedings of the 2009 International Conference on Machine Learning and Cybernetics., 5212497, Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, vol. 1, IEEE Computer Society, pp. 210-214, 8th International Conference on Machine Learning and Cybernetics, ICMLC 2009, Baoding, China, 2009/07/12. https://doi.org/10.1109/ICMLC.2009.5212497

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abstract = "Rough set theory (RST), support vector machine (SVM), and decision tree (DT) are brightly data mining methodologies for classification prediction tasks. While the accuracy for class prediction is highly emphasized, the ability to generate rules for decision support is also important in some practical applications. Studies have shown the ability of RST for feature selection while SVM and DT are significantly on their predictive power. Moreover, the ability of DT for rule generation is an attractive function. This study intents to integrate the advantages of RST, SVM and DT approaches to develop a hybrid data mining approach to improve the performance of class prediction as well as rule generation.",

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Improve class prediction performance using a hybrid data mining approach

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this