A sequential learning algorithm for self-adaptive resource allocation network classifier

This paper addresses sequential learning algorithm for self-adaptive resource allocation network classifier. Our approach makes use of self-adaptive error based control parameters to alter the training data sequence, evolve the network architecture, and learn the network parameters. In addition, the algorithm removes the training samples which are similar to the stored knowledge in the network. Thereby, it avoids the over-training problem and reduces the training time significantly. Use of misclassification information and hinge loss error in growing/learning criterion helps in approximating the decision function accurately. The performance evaluation using balanced and imbalanced data sets shows that the proposed algorithm generates minimal network with lesser computation time to achieve higher classification performance.