A Novel Incremental Class Learning Technique For Multi-Class Classification

In this paper, a novel technique for multi-class classification, which is independent of the number of class constraints and can learn the new classes it encounters, is developed. The developed technique enables remodelling of the network to adapt to the dynamic nature of non-stationary input samples. It not only can learn the new classes, but also the new patterns created in the input. The proposed algorithm is evaluated using various benchmark datasets and a comparative study of classification performance shows that the proposed algorithm is superior.