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. Proceedings of International Joint Conference on Neural Networks. International Joint Conference of Neural Networks 2009 , 14-19 June 2009, Atlanta, GA , pp. 2250-2255.
|PDF - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
View at Publisher Website: http://dx.doi.org/10.1109/IJCNN.2009.517...
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 Type:||Conference Item (Refereed Research Paper - E1)|
|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%|
|Deposited On:||19 Sep 2012 11:05|
|Last Modified:||19 Sep 2012 11:05|
Last 12 Months: 0
Repository Staff Only: item control page