Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams.

Mining streaming and drifting data is among the most popular contemporary applications of machine learning methods. Due to the potentially unbounded number of instances arriving rapidly, evolving concepts and limitations imposed on utilized computational resources, there is a need to develop efficient and adaptive algorithms that can handle such problems. These learning difficulties can be further augmented by appearance of skewed distributions during the stream progress. Class imbalance in non-stationary scenarios is highly challenging, as not only imbalance ratio may change over time, but also relationships among classes. In this paper we propose an efficient and fast cost-sensitive decision tree learning scheme for handling online class imbalance. In each leaf of the tree we train a perceptron with output adaptation to compensate for skewed class distributions, while McDiarmid's bound is used for controlling the splitting attribute selection. The cost matrix automatically adapts itself to the current imbalance ratio in the stream, allowing for a smooth compensation of evolving class relationships. Furthermore, we analyze characteristics of minority class instances and incorporate this information during the model update process. It allows our classifier to focus on most difficult instances, while a sliding window keeps track of changes in class structures. Experimental analysis carried out on a number of binary and multi-class imbalanced data streams indicate the usefulness of the proposed approach.