Online neural network model for non-stationary and imbalanced data stream classification

"Concept drift'' and class imbalance are two challenges for supervised classifiers. "Concept drift'' (or non-stationarity) is changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Previous methods for classifying non-stationary and imbalanced data streams mainly focus on batch solutions, in which the classification model is trained using a chunk of data. Here, we propose an online Neural Network (NN) model. The NN model, is composed of two different parts for handling concept drift and class imbalance. Concept drift is handled with a forgetting function and class imbalance is handled with a specific error function which assigns different importance to error in separate classes. The proposed method is evaluated on 3 synthetic and 8 real world datasets. The results show statistically significant improvement to previous online NN methods.