Survey on Pattern Optimization for Novel Class in MCM for Stream Data Classification

The classification of stream data is somehow difficult. Existing data stream classification techniques assume that total number of classes in the stream is fixed. Therefore, instances belonging to a novel class are misclassified by the existing techniques. Because data streams have endless length, conventional multi pass learning algorithms are not appropriate as they would require infinite storage and training time. Concept-drift occurs in the stream when the underlying concept of the data changes over time. Thus, the classification model must be updated continuously so that it reflects the most recent concept. In this paper we are presenting some efficient research approaches suggested by numerous scholars.