A self-adaptive classification method for concept-drifting data streams

A novel method was proposed for classifiying the concept-drifting data streams,which could track concept-drifting of data streams and quickly adapt to this change.After dividing a given data stream into several data blocks,it could choose the representative data from the first one for training model.The proposed method could alleviate the effects from noise and bordering data better,and be insensitive to outlier.Moreover,it used the created model for classifying each of the following data blocks,and used the classification results to dynamically adjust the current classification model.The experimental results showed that the proposed method could not only adjust classification model automatically according to the current status of data streams and quickly adapt to the situation of the concept drift,but also improve the classification performance.