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Publication Details

  Author(s): G. Tsoumakas, I. Katakis, I. Vlahavas.

Title: “Effective and Efficient Multilabel Classification in Domains with Large Number of Labels”.

Availability: Click here to download the PDF (Acrobat Reader) file (15 pages).


Appeared in: Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD'08), Antwerp, Belgium, 2008.

Abstract: This paper contributes a novel algorithm for effective and computationally efficient multilabel classification in domains with large label sets L. The HOMER algorithm constructs a Hierarchy Of Multilabel classifiERs, each one dealing with a much smaller set of labels compared to L and a more balanced example distribution. This leads to improved predictive performance along with linear training and logarithmic testing complexities with respect to |L|. Label distribution from parent to children nodes is achieved via a new balanced clustering algorithm, called balanced k means.

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