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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2013.1
- 수록면
- 108 - 121 (14page)
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Objectives: This paper is to examine breast cancer classification using support vector machine (SVM). SVM with optimal parameters obtained using the improved grid search with 5-fold cross validation has been proposed to reach the optimal classification performance.
Methods: Two data sets, Wisconsin Original Breast Cancer (WOBC) and Wisconsin Diagnostic Breast Cancer (WDBC) data set, were used to classify tumors as benign and malignant. SVM model performs the classification tasks using optimal kernel parameter and penalty parameter using 5-fold cross validation. Discriminant analysis, logistic regression analysis, decision tree, support vector machines were applied to analyze two data sets. Performance of these techniques was compared through accuracy, ROC curves and c-statistics.
Results: Our analysis showed that SVMs predicted breast cancer with highest accuracy and c-statistics among four classification models. A comparison of these SVMs indicated that SVM with optimal parameters has much superior performance than SVM with default parameters.
Conclusions: Research efforts have reported with increasing confirmation that SVMs have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on SVM with optimal parameters obtained using the improved grid search with 5-fold cross validation has been proposed. The performance of the method is evaluated using classification accuracy, ROC curves and c-statistics.
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참고문헌 (25)
참고문헌 신청
Anguita A / 2000 / Evaluating the generalization ability of support vector machines through the bootstrap / Neural Processing Letters 11 (1) : 51 ~ 58
Ayat NE / 2005 / Automatic model selection for the optimization of SVM kernels / Pattern Recognition 38 (10) : 1733 ~ 1745
Cevikalp H. / 2010 / New clustering algorithms for the support vector machine based hierarchical classification / Pattern Recognition Letters 31 (11) : 1285 ~ 1291
Fiuzy M / 2012 / Cancer based on fine needle aspiration (FNA) test data and combining intelligent systems / Iranian Journal of Cancer Prevention 4 : 169 ~ 177
Gestel TV / 2004 / Benchmarking least squares support vector machine classifiers / Machine Learning 54 : 5 ~ 32
Ayat NE / 2005 / Automatic model selection for the optimization of SVM kernels / Pattern Recognition 38 (10) : 1733 ~ 1745
Cevikalp H. / 2010 / New clustering algorithms for the support vector machine based hierarchical classification / Pattern Recognition Letters 31 (11) : 1285 ~ 1291
Fiuzy M / 2012 / Cancer based on fine needle aspiration (FNA) test data and combining intelligent systems / Iranian Journal of Cancer Prevention 4 : 169 ~ 177
Gestel TV / 2004 / Benchmarking least squares support vector machine classifiers / Machine Learning 54 : 5 ~ 32
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