Class imbalance problem is important issue in design of classifier. This problem results in classifier that gives more errors for minor class. Cost Sensitive approach is efficient to solve this problem in case of neural network. This paper studies effect of cost sensitive learning on Multilayer Perceptron classifier (MLP). The results show that, due to this approach minor class also acquires importance in learning resulting in accuracy of minor class also. Levenberg Marquardt (LM) using new computation (LM) is efficient method for weight updation. But this algorithm can’t be used with complex networks due to large memory requirement. This problem is solved by using new computation technique. Analysis shows that memory requirement has been reduced significantly.
Loading....