Nguwi, Yok-Yen, and Cho, Siu Yeung (2009) Support Vector Self-Organizing Learning for Imbalanced Medical Data. In: Proceedings of International Joint Conference on Neural Networks. pp. 2250-2255. From: International Joint Conference of Neural Networks 2009, 14-19 June 2009, Atlanta, GA.

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Abstract

The aim of computational learning algorithm is to establish grounds that works for any types of data, once and for all. However, majority of the classifiers assume the datasets are balanced. This research is targeted towards obtaining a model that is able to handle imbalanced data well. This work progresses by examining the efficiency of the model in evaluating imbalanced medical data. The model adopted a derivation of support vector machines in selecting variables. The classification phase uses unsupervised learning algorithm of Emergent Self-Organizing Map. Experimental results show that the criterion based on weight vector derivative achieves good results and performs consistently well over imbalance data.

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