A cluster and label approach for classifying imbalanced data streams in the presence of scarcely labelled data. (14th September 2022)

• Record Type: Journal Article
• Authors: Bhowmick, Kiran; Narvekar, Meera
• Is Part Of: International journal of business intelligence and data mining. Volume 21:Number 4(2022)
• Page Start: 443
• Page End: 464
• Publication Date: 2022-09-14
• Subjects: data streams -- classification -- imbalanced data -- semi-supervised learning -- scarcely labelled -- cluster and label -- micro cluster -- label propagation; 006.312
• Languages: English
• ISSNs: 1743-8187

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.