Feature Based Data Stream Classification (FBDC) and Novel Class Detection
نویسنده
چکیده
Data stream classification poses many challenges to the data mining community. Here this paper solves all the challenges such as infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream. Feature-evolution is a frequently occurring process in many streams, such as text streams, in which new features (i.e., words or phrases) appear as the stream progresses. Since a data stream classification is quite risky process, this paper can solve the challenges such as infinite length, concept-drift, concept-evolution, and feature-evolution, and classify them based on their features. All the data stream should classify based on their dynamic behaviour. It also enhances the novel class detection module by making it more adaptive to the evolving stream, and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach. Keywords— Data stream, concept-evolution, novel class, outlier
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