Handling Concept Drift and Feature Evolution in Textual Data Stream Using the Artificial Immune System

Data stream mining is an active research area that has attracted the attention of many researchers in the machine learning community. Discovering knowledge from large amounts of continuously generated data from online services and real time applications constitute a challenging task for data analytics where robust and efficient online algorithms are required. This paper presents a novel method for data stream mining. In particular, two main challenges of data stream processing are addressed, namely, concept drift and feature evolution in textual data streams. To address these issues, the proposed method uses the Artificial Immune System metaheuristic. AIS has powerful adapting capabilities which make it robust even in changing environments. Our proposed algorithm AIS-Clus has the ability to adapt its model to handle concept drift and feature evolution for textual data streams. Experimental results have been performed on textual dataset where efficient and promising results are obtained.

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