A cluster and label approach for classifying imbalanced data streams in the presence of scarcely labelled data Online publication date: Thu, 27-Oct-2022
by Kiran Bhowmick; Meera Narvekar
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 21, No. 4, 2022
Abstract: Classifying imbalanced data streams is often a challenging task primarily due to the continuous flow of infinite data and due to the unavailability of class labels. The problem is two-fold when the stream is imbalanced in nature. Due to the characteristics of data streams, it is impossible to store and process the data and deal with imbalance. There is a need to provide a solution that can consider the unavailability of class labels and classify the imbalanced data streams. This paper proposes a semi-supervised learning (SSL)-based model to classify scarcely labelled imbalanced data streams. A modified cluster and label SSL approach that uses expectation maximisation for clustering and similarity-based label propagation for labelling the unlabelled clusters is proposed. The model also employs a novel imbalance sensitive cluster merge technique to deal with the imbalance data. The results prove that the model outperforms standard stream classification algorithms.
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