최적의 서포트 벡터 머신을 이용한 유방암 분류
Breast Cancer Classification Using Optimal Support Vector Machine
1
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (37)
- "Breast Cancer"[URL]
- Annual report of cancer statistics in Korea in 2010, "Ministry of Health & Welfare", ~ (2012)
- Sewak M, "SVM approach to breast cancer classification", International Multi-Symposium of Computer and Computational Sciences (IMSCCS), 2, pp.32~37 (2007)
- Fiuzy M, "Cancer based on fine needle aspiration (FNA) test data and combining intelligent systems", Iranian Journal of Cancer Prevention, 4, pp.169~177 (2012)
- Yuan Y, "Induction of fuzzy decision trees", Fuzzy Sets and Systems, 69(2), pp.125~139 (1995)
- Vapnik VN., "The Nature of Statistical Learning Theory" (1996), John Wiley & Sons
- Zhang GP, "Neural networks for classification: a survey, IEEE transactions on systems, man and cybernetics—part C", Applications and Reviews, 30(4), pp.451~462 (2000)
- Gupta S, "Data mining classification techniques applied for breast cancer diagnosis and prognosis", Indian Journal of Computer Science and Engineering (IJCSE), 2(2), pp.188~195 (2011)
- Kitbumrungrat K., "Comparison logistic regression and discriminant analysis in classification groups for breast Cancer", Indian Journal of Computer Science and Engineering (IJCSE), 12(5), pp.111~115 (2012)
- Anguita A, "Evaluating the generalization ability of support vector machines through the bootstrap", Neural Processing Letters, 11(1), pp.51~58 (2000)
- Lin CT, "Fuzzy neural network classification design using support vector machine", Proceedings of the 2004 International Symposium on Circuits and Systems, pp.724~727 (2004)
- Martinez EF, "Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition", Engineering Applications of Artificial Intelligence, 19(6), pp.693~704 (2006)
- "Center for Machine Learning and Intelligent Systems"[URL]
- Gestel TV, "Benchmarking least squares support vector machine classifiers", Machine Learning, 54, pp.5~32 (2004)
- A practical guide to support vector classification, "Hsu CW", Technical report, Department of Computer Science and Information Engineering, University of National Taiwan, 1~12 (2003)
- Kalyani S, "Static security evaluation in power systems using multi-class SVM with different parameter selection methods", International Journal of Machine Learning and Computing (IJMLC), 1(2), pp.193~198 (2011)
- Ayat NE, "Automatic model selection for the optimization of SVM kernels", Pattern Recognition, 38(10), pp.1733~1745 (2005)
- Villa A, "Gradient optimization for multiple kernel’s parameters in support vector machines classification", International Geoscience and Remote Sensing Symposium (2008)
- Huang CL, "A GA-based feature selection and parameters optimization for support vector machines", Expert Systems with Applications, 31, pp.231~240 (2006)
- Lin SW, "Particle swarm optimization for parameter determination and feature selection of support vector machines", Expert Systems with Applications, 35, pp.1817~1824 (2008)
- Pai PP, "Support vector machines with simulated annealing algorithms in electricity load forecasting", Energy Conversion and Management, 46(17), pp.2669~2688 (2005)
- Ratnayake RMCB, "A SVM based method to detect color shift defects in IC packages", Conference on Machine Vision Applications, pp.138~141 (2007)
- Cevikalp H., "New clustering algorithms for the support vector machine based hierarchical classification", Pattern Recognition Letters, 31(11), pp.1285~1291 (2010)
- Yang CC, "Constructing a hybrid Kansei engineering system based on multiple affective responses: application to product form design", Computers & Industrial Engineering, 60, pp.760~768 (2011)
- Zhuang L, "Parameter optimization of kernel-based one-class classifier on imbalance learning", Journal of Computer, 1(7), pp.32~40 (2006)
- 김희훈, "Noise Removal using Support Vector Regression in Noisy Document Images", 응용통계연구, 25(4), pp.669~680 (2012), 한국통계학회
- Karatzoglou A, "Support vector machines in R", Journal of Statistical Software, 15(6), pp.1~28 (2006)
- Park Y, "On the derivation of propensity scales for predicting exposed transmembrane residues of helical membrane proteins", Bioinformatics, 23(6), pp.701~708 (2007)
- Zhang W, "Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster", BMC Bioinformatics, 9, pp.129~ (2008)
- Egan JP, "Signal decision theory and ROC analysis" (1975), Academic Press
- Cook NR, "Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve", Clinical Chemistry, 54(1), pp.17~23 (2008)
- "e1071: misc functions of the department of statistics (e1071), TU Wien. R package version 1.6-1"[URL]
- "lda: collapsed gibbs sampling methods for topic models. R package version 1.3.2"[URL]
- "rpart: recursive partitioning. R package version 4.1-1"[URL]
- Burges CA, "Tutorial on support vector machines for pattern recognition", Data Mining and Knowledge Discovery, 2, pp.121~167 (1998)
- Malorzata CJ, "Performance of the support vector machines for medical classification problems", Biocybernetics and Biomedical Engineering, 29(4), pp.63~81 (2009)
- Yoon HS, "A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer", Journal of Hydrology, 396(1-2), pp.128~138 (2011)