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Research Proposal on Incremental Learning-based Concept Drift Detection in Stream Classification

Research Proposal on Incremental Learning-based Concept Drift Detection in Stream Classification

   Stream data classification is the process of classifying the continuous flow of data from heterogeneous sources in real-time. Concept drift is a challenging issue in stream classification. Concept drift arises due to the change in the distribution of datasets over a period of time. Traditional methods to handle concept drift issues are unable to adapt to a sudden change in datasets and inability to learn continuously with real-time datasets.

   Incremental learning is more suitable to handle concept drift in stream data. Incremental learning is the learning model that adapts to new data without forgetting existing knowledge. Incremental learning learns from statistical characteristics change over time to detect the concept drift in stream classification. Concept drift detection in stream classification is performed using incremental learning with its sudden change adaptability and continuous learning ability to improve the performance.