A SURVEY ON VARIOUS CLASSIFICATION AND NOVEL CLASS DETECTION APPROACHES FOR FEATURE EVOLVING DATA STREAM

Abstract

The classification of data stream is challenging task for data mining community. Dynamic changing nature of data stream has some difficulties such as feature evolution, concept evolution, concept drift and infinite length. As we know that the data streams are huge in amount, it is impractical to store and use all the data for training. Concept drift occurs when underlying concept changes. Concept-evolution occurs as a result of new classes evolving in the stream. Another important characteristic of data streams, namely, feature evolution, in data stream new features emerge as stream advancement. In this paper we discuss the stream data classification processes and method of these classification techniques. Different authors used different method such as data miner and tree based approach for reduced such types of issues. Ensemble of classifier is used to detect novel classes for feature evolving data stream.