A Study of Supervised Learning with Multivariate Analysis on Unbalanced Datasets
Yu‐Yen Ou, Hao-Geng Hung, Yen‐Jen Oyang · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
How to handle unbalanced datasets and how to handle high-dimensional datasets are two of the most challenging issues faced by the latest machine learning research. This article reports a study aimed at providing effective solutions to these two challenges. For handling unbalanced datasets, we proposed that a different value of the cost parameter in Support Vector Machine (SVM) is employed for each class of samples. For handling high-dimensional datasets, we resorted to Independent Components Analysis (ICA), which is a multivariate analysis algorithm, along with the conventional univariate analysis. Experimental results confirmed that the proposed approaches all together significantly improved the prediction accuracy delivered by SVM.