A semi-supervised hierarchical classifier based on local information

• Published in: Pattern analysis and applications : PAA Vol. 27; no. 4
• Main Authors: Serrano-Pérez, Jonathan, Sucar, L. Enrique
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2024; Springer Nature B.V
• Subjects: Algorithms; Classification; Computer Science; Correlation coefficients; Labels; Machine learning; Pattern Recognition; Semi-supervised learning; Similarity; Statistical methods; Theoretical Advances; Hierarchical classification; Semi-supervised learning; Local distance
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-024-01345-1

The scarcity of labeled data is a common problem in supervised classification and in particular in hierarchical classification. Therefore, in this work a semi-supervised hierarchical classifier based on local information (SSHC-BLI) is proposed in order to take advantage of labeled and unlabeled data to perform classification tasks. SSHC-BLI is a semi-supervised learning algorithm for hierarchical classification, which tries to pseudo-label each unlabeled instance using the labels of its labeled neighbors, also, it uses a similarity function to determine whether the unlabeled instance is similar to its labeled neighbors to be pseudo-labeled; in this way, the heuristic function similarity of an instance with a set of instances is proposed. SSHC-BLI was tested in several datasets from different fields, including: artificial, functional genomics and text; also, it was compared against a supervised hierarchical classifier and  two state of the art methods, showing in most cases superior performance with statistical significance in exact match and Matthews correlation coefficient.