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

In this paper we evaluate the performance of multilabel classification algorithms on the EUR-Lex database of legal documents of the European Union. On the same set of underlying documents, we defined three different large-scale multilabel problems with up to 4000 classes. On these datasets, we compared three algorithms: (i) the well-known one-against-all approach (OAA); (ii) 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; and (iii) the multilabel pairwise perceptron algorithm (MLPP), which unlike the previous algorithms trains one base classifier for each pair of classes. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier. This makes them very suitable for large-scale multilabel classification problems. While previous work has already shown that the latter approach outperforms the other two ap...

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Eneldo Loza Mencía, Johannes Fürnkranz
• Keywords: Education, Large-scale Multilabel, LREC 2010, Multilabel Classification, Perceptron Algorithm