Ensemble learning for network data stream classification using similarity and online genetic algorithm classifiers

Abstract

The data are generated very rapidly from different information sources. These generation of data is increasing day by day from various sources such as automated data collection tools, database systems, e-commerce and social media websites. There is an explosive growth of data from terabytes to petabytes. It is essential to extract valuable knowledge from these large data. Since large amount of data is available, people look for valuable knowledge from the available data. Several mining algorithms are used to extract interesting patterns from the data stored in a repository. Traditional data sources are static in nature that the content are not generated very rapidly. Data streams are the streams of information that are generated at very rapid rate. After the evolution of data streams, the need arises to think of a new algorithm to process it. There are various data stream algorithms used for mining the data streams with different requirements. In this work, the ensemble of classifiers model has been developed for mining the data streams by combining stream mining classifiers such as Similarity-based Data Stream Classifier (SimC) and Online Genetic Algorithm (OGA) classifier. The performance of ensemble based classifiers show improved classification accuracy and less classification error rate under various circumstances.