Multilayer Perceptron Using Levenberg Marquardt Algorithm for Imbalanced Data

Supervised Learning algorithms learn from training dataset. If dataset has unequal distribution among number of samples of classes, classifier gives inaccurate results for minor class and correct results for major class. This paper studies the cost sensitive learning of Multilayer Perceptron (MLP) neural network to overcome this problem. This cost sensitive approach is for binary dataset which modifies the objective function. By this change, network produces accurate results for minor class also. Levenberg-Marquardt (LM) weight updation algorithm is used for network learning. It is fast and efficient algorithm for neural network training rather than other weight updation algorithms. Statistical results performed on real time dataset, shows that how fast the training is with LM algorithm. Also the result shows that MLP classifier produces accurate results for both major and minor dataset.