LaCova: A Tree-Based Multi-Label Classifier using Label Covariance as Splitting Criterion

Reem M Al-Otaibi, Meelis Kull, Peter A Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)


Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the challenge of exploiting correlations between labels. While in existing work these correlations are often modelled globally, in this paper we use the divide-and-conquer approach of decision trees which enables taking local decisions about how best to model label dependency. The resulting algorithm establishes a tree-based multi-label classifier called LaCova which dynamically interpolates between two well-known baseline methods: Binary Relevance, which assumes all labels independent, and Label Powerset, which learns the joint label distribution. The key idea is a splitting criterion based on the label covariance matrix at that node, which allows us to choose between a horizontal split (branching on a feature) and a vertical split (separating the labels). Empirical results on 12 data sets show strong performance of the proposed method, particularly on data sets with hundreds of labels.
Original languageEnglish
Title of host publicationInternational Conference on Machine Learning and Applications (ICMLA)
Place of PublicationDetroit, MI
PublisherIEEE Computer Society
Number of pages79
ISBN (Electronic)978-1-4799-7415-3
Publication statusPublished - 2014

Structured keywords

  • Jean Golding

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