IKnnM-DHecoc:A Method for Handling the Problem of Concept Drift
Uk B · Journal of Computer Research and Development · 2011
With the extensive applications of data stream mining,the classification of concept-drifting data streams has become more and more important and challenging.Due to the characteristics of data streams with concept-drifting,an effective learner should be able to track such changes and to quickly adapt to them.A method named dynamic hierarchical ECOC algorithm based on incremental KnnModel(IKnnM-DHecoc) for handling the problem of concept drift is proposed.It divides a given data stream into several data blocks,and then learns from each data block by using incremental KnnModel algorithm.Based on the outcomes of pre-learning,a hierarchical tree together with a hierarchical coding matrix are built and updated,from which a chosen incremental learning method is used for training in order to build a set of classifier and a set of classifier candidates.Moreover,a pruning strategy for generated nodes of hierarchical tree is proposed to reduce computational cost by taking account of each node's activity.In testing phase,a combination scheme of taking advantage of both IKnnModel and DHecoc is used for prediction.Experimental results show that the proposed IKnnM-DHecoc algorithm not only improves the dynamic nature of learning and classification performance,but could quickly adapt to the situation of concept drift.