A REVIEW OF TRADITIONAL AND SWARM SEARCH BASED FEATURE SELECTION ALGORITHMS FOR HANDLING DATA STREAM CLASSIFICATION

With the increase in recent development in hardware and software technologies, streaming data is used everywhere in today's environment and it is a very difficult task to store, process, investigate and visualize huge volumes of data. One of the most important and challenging issue in the data stream domain is the classification of the big datasets. However the conventional classification methods developed to run in a streaming environment with high use of memory constraints and longer execution running time. Another three major important issues in the data stream classification methods are huge length, conception drift and Feature Selection (FS). In this review paper, we consider the difficult problem of FS algorithms for streaming data, in which the size of streaming data for the feature set is unknown, primary to an inflexible demand in computation constraints, and not every feature is available from classifier model. In order to solve this difficulty, Swarm Intelligence (SI) algorithms are performed on the high dimensionality and streaming big dataset samples which result in increase classification accuracy, less memory consumption and lesser running time when compared to the existing streaming FS algorithms on various datasets. The proposed SI based FS algorithms overcomes the difficulty of the traditional FS algorithms.