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An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain

Loza Mencía, Eneldo and Fürnkranz, Johannes
Hinneburg, Alexander (ed.) (2007):
An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain.
In: Proceedings of the LWA 2007: Lernen - Wissen - Adaption, ISBN 978-3-86010-907-6,
[Online-Edition: http://www.ke.informatik.tu-darmstadt.de/publications/papers...],
[Conference or Workshop Item]

Abstract

In this paper we evaluate the performance of multilabel classification algorithms on two classification tasks related to documents of the EUR-Lex database of legal documents of the European Union. It permits different settings of large-scale multilabel problems with up to 4000 classes with the same underlying documents. We compared the well known one-against-all approach (OAA) and its recently proposed improvement, the multiclass multilabel perceptron algorithm (MMP), which modifies the OAA ensemble by respecting dependencies between the base classifiers in the training protocol of the classifier ensemble. Both use the simple but very efficient perceptron algorithm as underlying classifier. This makes them very suitable for large-scale multilabel classification problems, in particular when the number of classes is high. Our results on the EUR-Lex database confirm that the MMP algorithm has a better response to an increasing number of classes than the one-against-all approach. We also show that it is principally possible to efficiently and effectively handle very large multilabel problems.

Item Type: Conference or Workshop Item
Erschienen: 2007
Editors: Hinneburg, Alexander
Creators: Loza Mencía, Eneldo and Fürnkranz, Johannes
Title: An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain
Language: English
Abstract:

In this paper we evaluate the performance of multilabel classification algorithms on two classification tasks related to documents of the EUR-Lex database of legal documents of the European Union. It permits different settings of large-scale multilabel problems with up to 4000 classes with the same underlying documents. We compared the well known one-against-all approach (OAA) and its recently proposed improvement, the multiclass multilabel perceptron algorithm (MMP), which modifies the OAA ensemble by respecting dependencies between the base classifiers in the training protocol of the classifier ensemble. Both use the simple but very efficient perceptron algorithm as underlying classifier. This makes them very suitable for large-scale multilabel classification problems, in particular when the number of classes is high. Our results on the EUR-Lex database confirm that the MMP algorithm has a better response to an increasing number of classes than the one-against-all approach. We also show that it is principally possible to efficiently and effectively handle very large multilabel problems.

Title of Book: Proceedings of the LWA 2007: Lernen - Wissen - Adaption
ISBN: 978-3-86010-907-6
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Date Deposited: 24 Jun 2011 15:28
Official URL: http://www.ke.informatik.tu-darmstadt.de/publications/papers...
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