Research Article Open Access

Design of Output Codes for Fast Covering Learning using Basic Decomposition Techniques

Aruna Tiwari and Narendra S. Chaudhari

Abstract

We propose the design of output codes for solving the classification problem in Fast Covering Learning Algorithm (FCLA). For a complex multi-class problem normally the classifiers are constructed by combining the outputs of several binary ones. In this paper, we use the basic methods of decomposition; one per class (OPC) and Error Correcting Output Code (ECOC) with FCLA, binary to binary mapping algorithm as a base binary learner. The methods have been tested on Fisher’s well-known Iris data set and experimental results show that the classification ability is improved by using ECOC method.

Journal of Computer Science
Volume 2 No. 7, 2006, 565-571

DOI: https://doi.org/10.3844/jcssp.2006.565.571

Submitted On: 4 April 2006 Published On: 31 July 2006

How to Cite: Tiwari, A. & Chaudhari, N. S. (2006). Design of Output Codes for Fast Covering Learning using Basic Decomposition Techniques. Journal of Computer Science, 2(7), 565-571. https://doi.org/10.3844/jcssp.2006.565.571

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Keywords

  • Binary neural network
  • One per class
  • Error correcting output code