Research Repository

Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction

Thongkam, Jaree, Xu, Guandong, Zhang, Yanchun and Huang, Fuchun (2008) Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction. In: Advanced Web and NetworkTechnologies, and Applications : APWeb 2008 International Workshops: BIDM, IWHDM, and DeWeb Shenyang, China, April 26-28, 2008. Revised Selected Papers. Ishikawa, Yoshiharu, He, Jing, Xu, Guandong, Shi, Yong, Huang, Guangyan, Pang, Chaoyi, Zhang, Qing and Wang, Guoren, eds. Lecture Notes in Computer Science (4977). Springer, Berlin, pp. 99-109.

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Finding and removing misclassified instances are important steps in data mining and machine learning that affect the performance of the data mining algorithm in general. In this paper, we propose a C-Support Vector Classification Filter (C-SVCF) to identify and remove the misclassified instances (outliers) in breast cancer survivability samples collected from Srinagarind hospital in Thailand, to improve the accuracy of the prediction models. Only instances that are correctly classified by the filter are passed to the learning algorithm. Performance of the proposed technique is measured with accuracy and area under the receiver operating characteristic curve (AUC), as well as compared with several popular ensemble filter approaches including AdaBoost, Bagging and ensemble of SVM with AdaBoost and Bagging filters. Our empirical results indicate that C-SVCF is an effective method for identifying misclassified outliers. This approach significantly benefits ongoing research of developing accurate and robust prediction models for breast cancer survivability.

Item Type: Book Section
ISBN: 9783540893752 (print) 9783540893769 (online)
Uncontrolled Keywords: ResPubID14748, outlier detection system, outliers filtering framework, C-Support Vector Classification Filter, C-SVCF, algorithms, Thailand
Subjects: Current > FOR Classification > 0807 Library and Information Studies
Current > FOR Classification > 1117 Public Health and Health Services
Historical > SEO Classification > 8903 Information Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 19 Nov 2013 01:30
Last Modified: 15 Dec 2014 01:40
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Citations in Scopus: 32 - View on Scopus

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