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- W4313011199 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." @default.
- W4313011199 created "2023-01-05" @default.
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- W4313011199 date "2022-01-01" @default.
- W4313011199 modified "2023-10-14" @default.
- W4313011199 title "A cluster and label approach for classifying imbalanced data streams in the presence of scarcely labelled data" @default.
- W4313011199 doi "https://doi.org/10.1504/ijbidm.2022.126503" @default.
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