Design of Output Codes for Fast Covering Learning using Basic Decomposition Techniques
Aruna Tiwari and Narendra S. Chaudhari
DOI : 10.3844/jcssp.2006.565.571
Journal of Computer Science
Volume 2, Issue 7
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.
© 2006 Aruna Tiwari and Narendra S. Chaudhari. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.