Extending attribute information for small data set classification

Der Chiang Li, Chiao Wen Liu

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Data quantity is the main issue in the small data set problem, because usually insufficient data will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. This paper proposes a new attribute construction approach which converts the original data attributes into a higher dimensional feature space to extract more attribute information by a similarity-based algorithm using the classification-oriented fuzzy membership function. Seven data sets with different attribute sizes are employed to examine the performance of the proposed method. The results show that the proposed method has a superior classification performance when compared to principal component analysis (PCA), kernel principal component analysis (KPCA), and kernel independent component analysis (KICA) with a Gaussian kernel in the support vector machine (SVM) classifier.

Original languageEnglish
Article number5677515
Pages (from-to)452-464
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
Publication statusPublished - 2012 Feb 6

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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