An unsupervised self-organizing learning with support vector ranking for imbalanced datasets

Yok Yen Nguwi, Siu Yeung Cho

Research output: Journal PublicationArticlepeer-review

24 Citations (Scopus)


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 support vector 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.

Original languageEnglish
Pages (from-to)8303-8312
Number of pages10
JournalExpert Systems with Applications
Issue number12
Publication statusPublished - Dec 2010
Externally publishedYes


  • Emergent Self-Organizing Map
  • Imbalanced datasets
  • Support vector ranking

ASJC Scopus subject areas

  • Engineering (all)
  • Computer Science Applications
  • Artificial Intelligence


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