Streaming Big Data Processing Using Improved Harmony Search

Streaming big data concerns large-volume, growing and complex data sets. Most attention on the data stream classification paid on non-evolutionary methods. In this paper, we introduce new incremental learning algorithms based on harmony search. We first propose a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, we improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise. This improved version is called improved incremental harmony-based classifier. The proposed methods are evaluated on some real world data sets. Experimental results show that the proposed incremental methods can effectively address the issues usually encountered in the streaming big data environments.