A Study of Supervised Learning with Multivariate Analysis on Unbalanced Datasets.
Journal
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16-21 July 2006
Pages
2201-2205
Date Issued
2006
Author(s)
Abstract
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.
Type
conference paper