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.
Highlights
In the last two decades, binary neural networks (BNNs) have attracted attention of many researchers and there have been many established approaches for the construction of BNNs
We show that the use of Error Correcting Output Code (ECOC) method for Fast Covering Learning Algorithm (FCLA)
For combining the outputs of the hidden layer neurons, FCLA approach can be extended for the training of output layer by using either of the two coding schemes: During learning, the hidden layer neurons are trained using two class learning algorithm to learn each of gj function of x1,x2,....,xm examples
Summary
In the last two decades, binary neural networks (BNNs) have attracted attention of many researchers and there have been many established approaches for the construction of BNNs. In the training stage , we need to construct hidden layer by independent K binary classifiers where K is the number of classes to be learned. For combining the outputs of the hidden layer neurons, FCLA approach can be extended for the training of output layer by using either of the two coding schemes: During learning, the hidden layer neurons are trained using two class learning algorithm to learn each of gj function of x1,x2,....,xm examples. The output layer neurons are trained depending on the coding scheme used for the classification OPC or ECOC, presented . if(t2
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