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Semi-supervised Self-organizing Feature Map for Gene Expression Data Classification

  • Moumita Roy
  • Anwesha Law
  • Susmita Ghosh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

In this article, a one-dimensional self-organizing feature map (SOFM) neural network integrated with semi-supervised learning is used to predict the class label of gene expression data under the scarcity of the labeled patterns. Iterative learning of the semi-supervised SOFM network is carried out using a few labeled patterns along with some selected unlabeled patterns. The unlabeled patterns, for which the maximum target value is greater than a threshold, are selected as the confident ones. Results are found to be encouraging.

Keywords

Semi-supervised learning gene expression data self- organizing feature map 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Moumita Roy
    • 1
  • Anwesha Law
    • 1
  • Susmita Ghosh
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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