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Title: Effective and Efficient Multilabel Classification in Domains with Large Number of Labels
Author(s): G. Tsoumakas, I. Katakis, I. Vlahavas.
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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