Active learning for hierarchical multi-label classification

• Published in: Data mining and knowledge discovery Vol. 34; no. 5; pp. 1496 - 1530
• Main Authors: Nakano, Felipe Kenji, Cerri, Ricardo, Vens, Celine
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Active learning; Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Classification; Cognitive tasks; Computer Science; Data Mining and Knowledge Discovery; Data points; Domains; Information Storage and Retrieval; Journal Track of ECML PKDD 2020; Labels; Machine learning; Performance prediction; Physics; Statistics for Engineering; Active learning; Hierarchical multi-label classification; Predictive clustering trees
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-020-00704-w

Due to technological advances, a massive amount of data is produced daily, presenting challenges for application areas where data needs to be labelled by a domain specialist or by expensive procedures, in order to be useful for supervised machine learning purposes. In order to select which data points will provide more information when labelled, one can make use of active learning methods. Active learning (AL) is a subfield of machine learning which addresses methods to build models with fewer, but more representative instances. Even though AL has been vastly studied, it has not been thoroughly investigated in hierarchical multi-label classification, a learning task where multiple class labels can be assigned to an instance and these labels are hierarchically structured. In this work, we provide a public framework containing baseline and state-of-the-art algorithms suitable for this task. Additionally, we also propose a new algorithm, namely Hierarchical Query-By-Committee (H-QBC), which is validated on datasets from different domains. Our results show that H-QBC is capable of providing superior predictive performance results compared to its competitors, while being computationally efficient and parameter free.