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Binary relevance efficacy for multilabel classification

Binary relevance efficacy for multilabel classification The goal of multilabel (ML) classification is to induce models able to tag objects with the labels that better describe them. The main baseline for ML classification is binary relevance (BR), which is commonly criticized in the literature because of its label independence assumption. Despite this fact, this paper discusses some interesting properties of BR, mainly that it produces optimal models for several ML loss functions. Additionally, we present an analytical study of ML benchmarks datasets and point out some shortcomings. As a result, this paper proposes the use of synthetic datasets to better analyze the behavior of ML methods in domains with different characteristics. To support this claim, we perform some experiments using synthetic data proving the competitive performance of BR with respect to a more complex method in difficult problems with many labels, a conclusion which was not stated by previous studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Progress in Artificial Intelligence Springer Journals

Binary relevance efficacy for multilabel classification

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References (9)

Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Data Mining and Knowledge Discovery; Computer Imaging, Vision, Pattern Recognition and Graphics; Language Translation and Linguistics; Control, Robotics, Mechatronics; Artificial Intelligence (incl. Robotics); Computational Intelligence
ISSN
2192-6352
eISSN
2192-6360
DOI
10.1007/s13748-012-0030-x
Publisher site
See Article on Publisher Site

Abstract

The goal of multilabel (ML) classification is to induce models able to tag objects with the labels that better describe them. The main baseline for ML classification is binary relevance (BR), which is commonly criticized in the literature because of its label independence assumption. Despite this fact, this paper discusses some interesting properties of BR, mainly that it produces optimal models for several ML loss functions. Additionally, we present an analytical study of ML benchmarks datasets and point out some shortcomings. As a result, this paper proposes the use of synthetic datasets to better analyze the behavior of ML methods in domains with different characteristics. To support this claim, we perform some experiments using synthetic data proving the competitive performance of BR with respect to a more complex method in difficult problems with many labels, a conclusion which was not stated by previous studies.

Journal

Progress in Artificial IntelligenceSpringer Journals

Published: Oct 14, 2012

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