Support Vector Self-Organizing Learning for Imbalanced Medical Data

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.

Item ID: 22720
Item Type: Conference Item (Refereed Research Paper - E1)
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ISBN: 978-1-4244-3553-1
ISSN: 1098-7576
Date Deposited: 19 Sep 2012 01:05
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%
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