Cost-sensitive multi-layer perceptron for binary classification with imbalanced data

Currently, class imbalance has been a challenge for classification due to its highly imbalanced instances of distinct classes. With the advantage in quantity, the majority classes can get high accuracy in classification while many instances belonging to minority classes are inclined to be classified as majority classes. In this paper, we propose a novel cost-sensitive method based on multi-layer perceptron (CMMLP) for binary classification with Unbalanced data. The proposed cost matrix is used to modify the construction of loss function so as to encourage classifier to pay more attention to the accuracy of minority class by minimizing training error. in order to verify the effectiveness of CMMLP, CMMLP is applied to some benchmark datasets and Click-Through Rate (CTR) prediction datasets. Experimental results illustrate that the new cost-sensitive approach can achieve better performance for binary classification with Unbalanced data than the original MLP (OMLP).