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Pairwise Learning of Multilabel Classifications with Perceptrons

Loza Mencía, Eneldo and Fürnkranz, Johannes (2007):
Pairwise Learning of Multilabel Classifications with Perceptrons.
[Online-Edition: http://www.ke.informatik.tu-darmstadt.de/publications/report...],
[Report]

Abstract

Multiclass Multilabel Perceptrons (MMP) are an efficient incremental algorithm for training a team of perceptrons for a multilabel prediction task. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative approach that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. An evaluation on the Reuters 2000 (RCV1) data shows that our multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example.

Item Type: Report
Erschienen: 2007
Creators: Loza Mencía, Eneldo and Fürnkranz, Johannes
Title: Pairwise Learning of Multilabel Classifications with Perceptrons
Language: English
Abstract:

Multiclass Multilabel Perceptrons (MMP) are an efficient incremental algorithm for training a team of perceptrons for a multilabel prediction task. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative approach that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. An evaluation on the Reuters 2000 (RCV1) data shows that our multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Date Deposited: 24 Jun 2011 15:25
Official URL: http://www.ke.informatik.tu-darmstadt.de/publications/report...
Identification Number: TUD-KE-2007-05
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