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
Pages	210-214
Number of pages	5
DOIs	https://doi.org/10.1109/ICMLC.2009.5212497
Publication status	Published - 2009
Externally published	Yes
Event	2009 International Conference on Machine Learning and Cybernetics - 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	2009 International Conference on Machine Learning and Cybernetics
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

Improve class prediction performance using a hybrid data mining approach. / Chen, Li Fei.
Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. 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, pp. 210-214, 2009 International Conference on Machine Learning and Cybernetics, Baoding, China, 2009/07/12. https://doi.org/10.1109/ICMLC.2009.5212497

@inproceedings{275638834e6f4f8abf075b86dda198d3,

title = "Improve class prediction performance using a hybrid data mining approach",

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.",

keywords = "Classification, Data mining, Decision trees, Rough set theory, Rule generation, Support vector machine",

author = "Chen, {Li Fei}",

year = "2009",

doi = "10.1109/ICMLC.2009.5212497",

language = "English",

isbn = "9781424437030",

series = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

pages = "210--214",

booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

note = "2009 International Conference on Machine Learning and Cybernetics ; Conference date: 12-07-2009 Through 15-07-2009",

}

TY - GEN

T1 - Improve class prediction performance using a hybrid data mining approach

AU - Chen, Li Fei

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Classification

KW - Data mining

KW - Decision trees

KW - Rough set theory

KW - Rule generation

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=70350729113&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70350729113&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2009.5212497

DO - 10.1109/ICMLC.2009.5212497

M3 - Conference contribution

AN - SCOPUS:70350729113

SN - 9781424437030

T3 - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

SP - 210

EP - 214

BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

T2 - 2009 International Conference on Machine Learning and Cybernetics

Y2 - 12 July 2009 through 15 July 2009

ER -

Improve class prediction performance using a hybrid data mining approach

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this