Fuzzy PCA and Support Vector Machines for Breast Cancer Classification
Keywords:Feature Selection, classification, accuracy, Fuzzy PCA, SVM.
Breast cancer is the leading cause of death among women in the world and early detection can increase the chance of survival for the patients. However, expert system and machine learning diagnosis are burdened with the presence of irrelevant data and noise which can reduce the accuracy of prediction and increase computational time. In this paper, Fuzzy Principle Component Analysis (FPCA) and Support Vector Machines (SVM) are proposed for the classification of breast cancer dataset. Experimental results on public breast cancer dataset show that the proposed method FPCA-SVM outperformed the benchmark models in terms of accuracy, specificity, and sensitivity and AUC value. The proposed model can assist doctors and medical practitioners for an early detection of breast cancer.
 Polat, K., et al. (2007). "Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism." Expert Systems with Applications 32(1): 172-183.
 Chen, H.-L., et al. (2011). "A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis." Expert Systems with Applications 38(7): 9014-9022.
 Sun, Y. and D. Wu (2008). "A RELIEF based feature extraction algorithm." Proceedings of the 8th SIAM International Conference on Data Mining: 188â€“195.
 Tanatavikorn, H. and Y. Yamashita (2016). "Fuzzy Treatment Method for Outlier Detection in Process Data." Journal of Chemical Engineering of Japan 49(9): 864-873.
 Xu, L. and A. L. Yuille (1995). "Robust principal component analysis by self-organizing rules based on statistical physics approach." IEEE Transactions on Neural Networks 6(1): 131-143.
 Yang, T.-N. and S.-D. Wang (1999). "Robust algorithms for principal component analysis." Pattern Recognition Letters 20(9): 927-933.
 Luukka, P. (2010). "Nonlinear fuzzy robust PCA algorithms and similarity classifier in bankruptcy analysis." Expert Systems with Applications 37(12): 8296-8302.