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Eneldo Loza Mencía
Researcher at Technische Universität Darmstadt
Publications - 74
Citations - 2489
Eneldo Loza Mencía is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Multi-label classification & Pairwise comparison. The author has an hindex of 16, co-authored 74 publications receiving 2049 citations. Previous affiliations of Eneldo Loza Mencía include Aristotle University of Thessaloniki.
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Journal ArticleDOI
Multilabel classification via calibrated label ranking
TL;DR: This work proposes a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of existing approaches and suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique.
Book ChapterDOI
Large-scale Multi-label Text Classification - Revisiting Neural Networks
TL;DR: This paper proposed to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks, and showed that BP-MLL's ranking loss can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting.
Book ChapterDOI
DeepRED – Rule Extraction from Deep Neural Networks
TL;DR: A new decompositional algorithm – DeepRED – is introduced that is able to extract rules from deep neural networks that are easy to understand and understandable.
Book ChapterDOI
Large-scale multi-label text classification — revisiting neural networks
TL;DR: It is shown that BP-MLL's ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting.
Book ChapterDOI
Efficient pairwise multilabel classification for large-scale problems in the legal domain
TL;DR: This paper applies multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union and resorts to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size.