Advertisement

Constructive Semi-Supervised Classification Algorithm and Its Implement in Data Mining

  • Arvind Singh Chandel
  • Aruna Tiwari
  • Narendra S. Chaudhari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

In this paper, we propose a novel fast training algorithm called Constructive Semi-Supervised Classification Algorithm (CS-SCA) for neural network construction based on the concept of geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. It’s a semi-supervised based approach, the training samples are semi-labeled i.e. for some samples, labels are known and for some samples, data labels are not known. The method starts with clustering, which is done by using the concept of geometrical expansion. In clustering process various clusters are formed. The clusters are visualizes in terms of hyperspheres. Once clustering process over labeling of hyperspheres is done, in which class is assigned to each hypersphere for classifying the multi-dimensional data. This constructive learning avoids blind selection of neural network structure. The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. Through our experimental work we conclude that CS-SCA result in simple neural network structure by less training time.

Keywords

Semisupervised classification Geometrical Expansion Binary Neural Network Hyperspheres 

References

  1. 1.
    Wang, D., Chaudhari, N.S.: A Constructive Unsupervised Learning Algorithm for Clustering Binary Patterns. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), Budapest, July 2004, vol. 2, pp. 1381–1386 (2004)Google Scholar
  2. 2.
    Wang, D., Chaudhari, N.S.: A Novel Training Algorithm for Boolean Neural Networks Based on Multi-Level Geometrical Expansion. Neurocomputing 57C, 455–461 (2004)CrossRefGoogle Scholar
  3. 3.
    Kim, J.H., Park, S.K.: The geometrical learning of binary neural neworks. IEEE Transaction. Neural Networks 6, 237–247 (1995)CrossRefGoogle Scholar
  4. 4.
    Joo Er, M., Wu, S., Yang, G.: Dynamic Fuzzy Neural Networks. McGraw-Hill, New York (2003)Google Scholar
  5. 5.
    Kwok, T.Y., Yeung, D.Y.: Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Networks 8, 630–645 (1997)CrossRefGoogle Scholar
  6. 6.
    Chaudhari, N.S., Tiwari, A., Thomus, J.: Performance Evaluation of SVM Based Semi-supervised Classification Algorithm. In: International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietman, December 17-20 (2008)Google Scholar
  7. 7.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Arvind Singh Chandel
    • 1
  • Aruna Tiwari
    • 1
  • Narendra S. Chaudhari
    • 2
  1. 1.Department of Computer Engg.Shri GS Inst of Tech.& Sci.IndoreIndia
  2. 2.Department of Computer Science and Engineering (CSE)IIT, IndoreIndore

Personalised recommendations