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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 Fishers well-known Iris data set and experimental results show that the classification ability is improved by using ECOC method

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