A Stochastic Gradient Descent Based SVM with Fuzzy-Rough Feature Selection and Instance Selection for Breast Cancer Diagnosis

摘要

Breast cancer remains to be one of the most severe and deadly diseases among women in the world. Fortunately, a long survival rate for patients with not metastasized breast cancer can be achieved with the help of early detection, proper treatment and therapy. This urges the need to develop efficient classification models with high predictive performance. Machine learning and artificial intelligence based methods are effectively utilized for building classification models in medical domain. In this paper, fuzzy-rough feature selection based support vector machine classifier with stochastic gradient descent learning is proposed for breast cancer diagnosis. In the proposed model, fuzzy-rough feature selection with particle swarm optimization based search is used for obtaining a subset of relevant features for model. In order to select appropriate instances, a fuzzy-rough instance selection method is utilized. The effectiveness of the proposed classification approach is evaluated on Wisconsin Breast Cancer Dataset (WBCD) with classification evaluation metrics, such as classification accuracy, sensitivity, specificity, F-measure and kappa statistics. Experimental results indicate that the proposed model can achieve a very high predictive performance.