An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
Nguwi, Yok-Yen, and Cho, Siu-Yeung (2010) An unsupervised self-organizing learning with support vector ranking for imbalanced datasets. Expert Systems and Application, 37 (12). pp. 8303-8312.
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The aim of computational learning algorithm is to establish grounds that work for any types of data, once and for all. However, majority of the classifiers have their base from balanced datasets. This paper discusses the issues related to imbalanced data distribution problem and the common strategy to deal with imbalance datasets. We propose a model capable of handling imbalance datasets well in which other typical classifiers fail to do so. The model adopted a derivation of supportvector machines in selecting variables so that the problem of imbalanced data distribution can be relaxed. Then, we used an Emergent Self-Organizing Map (ESOM) to cluster the ranker features so as to provide clusters for unsupervised classification. This work progresses by examining the efficiency of the model in evaluating imbalanced datasets. Experimental results show that the criterion based on weight vector derivative achieves good results and performs consistently well over imbalance datasets. In general, our approach outperforms other classification methods which are unable to handle the imbalanced data distribution in the testing datasets.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||imbalanced datasets; support vector ranking; emergent self-organizing map|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100%|
|Deposited On:||25 Jun 2012 16:36|
|Last Modified:||25 Jun 2012 16:36|
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