Self-paced multi-label co-training

Document Type

Article

Publication Date

4-1-2023

Abstract

Multi-label learning aims to solve classification problems where instances are associated with a set of labels. In reality, it is generally easy to acquire unlabeled data but expensive or time-consuming to label them, and this situation becomes more serious in multi-label learning as an instance needs to be annotated with several labels. Hence, semi-supervised multi-label learning approaches emerge as they are able to exploit unlabeled data to help train predictive models. This work proposes a novel approach called Self-paced Multi-label Co-Training (SMCT). It leverages the well-known co-training paradigm to iteratively train two classifiers on two views of a dataset and communicate one classifier's predictions on unlabeled data to augment the other's training set. As pseudo labels may be false in iterative training, self-paced learning is integrated into SMCT to rectify false pseudo labels and avoid error accumulation. Concretely, the multi-label co-training model in SMCT is formulated as an optimization problem by introducing latent weight variables of unlabeled instances. It is then solved via an alternative convex optimization algorithm. Experimental evaluations are carried out based on six benchmark multi-label datasets and three metrics. The results demonstrate that SMCT is very competitive in each setting when compared with five state-of-the-art methods.

Identifier

85143748297 (Scopus)

Publication Title

Information Sciences

External Full Text Location

https://doi.org/10.1016/j.ins.2022.11.153

ISSN

00200255

First Page

269

Last Page

281

Volume

622

Grant

61672117

Fund Ref

National Natural Science Foundation of China

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