An effective single-model learning for multi-label data

• Published in: Expert systems with applications Vol. 232; p. 120887
• Main Authors: Siahroudi, Sajjad Kamali, Kudenko, Daniel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Deep learning; Imbalanced data; Multi-label learning; Deep learning; Multi-label learning; Imbalanced data
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.120887

Multi-label data classification (MLC) has become an increasingly active research area over the past decade. MLC refers to a classification problem where each instance can be associated with more than one class label. Capturing the correlation among labels and tackling the label imbalance are the main challenges in MLC. Problem transformation is one of the well-known approaches in this area that became the de-facto approach for MLC. Existing methods in this approach consider MLC as a collection of single-label tasks and solve each of them separately. To consider correlation among labels, some of them consider the combination of labels that appear in the training data as a separate label. The main drawback of these kinds of methods is the complexity of the model, which makes them not applicable in real-world applications. In this paper, we show how MLC can be efficiently and effectively tackled with a single classifier. Our proposed method maps the training data into a new sub-space for each label. Then, it pools all the mapped data together and efficiently trains a single classifier for all the labels together. Experimental results show that our method successfully tackles MLC tasks and outperforms the state-of-the-art methods. •Learning Multi-label data with a novel and efficient single model.•Generate an orthogonal transformed matrix for each label as its context (key).•Generate single label training data for each label in its context.•Deal with imbalanced labels by down-sampling based on a neighborhood graph.•Show the effectiveness of our method on various datasets in different scenarios.