Combining Semi-supervised Clustering and Classification Under a Generalized Framework

Zhen Jiang (), Lingyun Zhao () and Yu Lu ()
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Zhen Jiang: Jiangsu University
Lingyun Zhao: Jiangsu University
Yu Lu: Jiangsu University

Journal of Classification, 2025, vol. 42, issue 1, No 10, 204 pages

Abstract: Abstract Most machine learning algorithms rely on having a sufficient amount of labeled data to train a reliable classifier. However, labeling data is often costly and time-consuming, while unlabeled data can be readily accessible. Therefore, learning from both labeled and unlabeled data has become a hot topic of interest. Inspired by the co-training algorithm, we present a learning framework called CSCC, which combines semi-supervised clustering and classification to learn from both labeled and unlabeled data. Unlike existing co-training style methods that construct diverse classifiers to learn from each other, CSCC leverages the diversity between semi-supervised clustering and classification models to achieve mutual enhancement. Existing classification algorithms can be easily adapted to CSCC, allowing them to generalize from a few labeled data. Especially, in order to bridge the gap between class information and clustering, we propose a semi-supervised hierarchical clustering algorithm that utilizes labeled data to guide the process of cluster-splitting. Within the CSCC framework, we introduce two loss functions to supervise the iterative updating of the semi-supervised clustering and classification models, respectively. Extensive experiments conducted on a variety of benchmark datasets validate the superiority of CSCC over other state-of-the-art methods.

Keywords: Co-training; Classification; Semi-supervised clustering; Cluster-splitting (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-024-09489-9

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