Robust bounded logistic regression in the class imbalance problem
In this paper, we propose to deal with the problems of logistic regression with outliers and class imbalance, which are common in a wide range of practical applications. The robust bounded logistic regression with different error costs is developed to reduce the combined influence of outliers and class imbalance. First, inspired by the Correntropy induced loss function, we develop the bounded logistic loss function which is a monotonic, bounded and nonconvex loss and thus robust to outliers. With the bounded logistic loss, we construct a new robust logistic regression. Second, under the principle of cost-sensitive learning, we assign different error costs for different classes in order to reduce the sensitiveness of the new robust logistic regression to class imbalance. Using the half-quadratic optimization method, it is easy to optimize the proposed logistic regression model. Experimental results demonstrate that our proposed method improves the performance of logistic regression on the datasets with outliers and class imbalance.