Algorithm of Recurring Concept Drift Base on Main Feature Extraction

Recurring concept drift is one of the sub-types of concept drift. In recurring concept drift detection, it is very important to represent concepts and select the most appropriate classifier to classify. We propose an algorithm, conceptual clustering and prediction through main feature extraction (MFCCP), for classifying data stream with recurring concept drifts. MFCCP can recognize recurring concepts by computing the differences of main features and impact factors of different batches of samples. It maintains a classifier for each concept and monitors the classification accuracy to select classifier according to hoeffding inequality in order to enhance the ability of adapting to concept drift. The experimental results over the three datasets illustrate that MFCCP achieves better classification accuracy, adapts faster to concept drift, and detects concept drift more accurately than the other four algorithms on the data streams with recurring concept drifts, and therefore, MFCCP is apt to classify data stream without recurring concept drift.