SVM and SVM Ensembles in Breast Cancer Prediction

SVM and SVM Ensembles in Breast Cancer Prediction

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary ...

Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles

Learning from imbalanced datasets is inherently difficult due to lack of information about the minority class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs suffer from biased decision boundaries, and that their prediction performance drops dramatically whe...

Bias-Variance Analysis and Ensembles of SVM

Bankruptcy Prediction using SVM and Hybrid SVM Survey

Bankruptcy prediction has been a topic of active research for business and corporate organizations since past decades. It is an effective tool to help financial institutions and relevant people to make the right decision in investments, especially in the current competitive environment. The tool provides auditors and managers a chance to identify the problems early.

Diversified SVM Ensembles for Large Data Sets

Recently, the core vector machine (CVM) has shown significant speedups on classification and regression problems with massive data sets. Its performance is also almost as accurate as other state-ofthe-art SVM implementations. By incorporating the orthogonality constraints to diversify the CVM ensembles, this turns out to speed up the maximum margin discriminant analysis (MMDA) algorithm. Extens...