Novel class detection in data streams using local patterns and neighborhood graph

Data stream classification is one of the most challenging areas in the machine learning. In this paper, we focus on three major challenges namely infinite length, concept-drift and concept-evolution. Infinite length causes the inability to store all instances. Concept-drift is the change in the underlying concept and occurs in almost every data stream. Concept-evolution, in fact, is the arrival of novel classes and is an undeniable phenomenon in most real world data streams. There are lots of researches about data stream classification, but most of them focus on the first two challenges and ignore the last one. In this paper, we propose new method based on ensembles whose classifiers use local patterns to enhance the accuracy. Local pattern is a group of Boolean features which have local influence on ordinal and categorical features. Also, in order to enhance the accuracy of novel class detection we construct a neighborhood graph among novel class candidates and analyze connected components of the constructed graph. Experiments on both real and synthetic benchmark data sets show the superiority of the proposed method over the related state-of-the-art techniques.