A new binary classifier: clustering-launched classification

Authors:
Tung-Shou Chen;Chih-Chiang Lin;Yung-Hsing Chiu;Hsin-Lan Lin;Rong-Chang Chen
Affiliations:
Graduate School of Computer Science and Information Technology, National Taichung Institute of Technology;Graduate School of Computer Science and Information Technology, National Taichung Institute of Technology;Graduate School of Computer Science and Information Technology, National Taichung Institute of Technology;Graduate School of Business Administration, National Taichung Institute of Technology;Department of Logistics Engineering and Management, National Taichung Institute of Technology
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 7
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Data mining: concepts and techniques

Data mining: concepts and techniques
Data Mining: Introductory and Advanced Topics

Data Mining: Introductory and Advanced Topics
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Novel questionnaire-responded transaction approach with SVM for credit card fraud detection

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Mathematical analysis of classifying convex clusters based on support functionals

ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Building an intrusion detection system based on support vector machine and genetic algorithm

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III

Referential hierarchical clustering algorithm based upon principal component analysis and genetic algorithm

ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Prediction model building with clustering-launched classification and support vector machines in credit scoring

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the powerful classifiers is Support Vector Machine (SVM), which has been successfully applied to many fields. Despite its remarkable achievement, SVM is time-consuming in many situations where the data distribution is unknown, causing it to spend much time on selecting a suitable kernel and setting parameters. Previous studies proposed understanding the data distribution before classification would assist the classification. In this paper, we exquisitely combined with clustering and classification to develop a novel classifier, Clustering-Launched Classification (CLC), which only needs one parameter. CLC employs clustering to group data to characterize the features of the data and then adopts the one-against-the-rest and nearest-neighbor to find the support vectors. In our experiments, CLC is compared with two well-known SVM tools: LIBSVM and mySVM. The accuracy of CLC is comparable to LIBSVM and mySVM. Furthermore, CLC is insensitive to parameter, while the SVM is sensitive, showing CLC is easier to use.