Minimalistic Homepage of Eneldo Loza Mencía
- Contact
- eneldo@ke.tu-darmstadt.de
- Research Interests
- Multilabel, Large-Scale, Pairwise and Text Classification, Rule Learning, Deep Learning, Understandable and Interpretative Models, Text Summarization, Web Mining
- Selected Publications
- The effective Multilabel Pairwise Perceptrons (MLPP) algorithm on the large Reuters RCV1 dataset:
and , Pairwise Learning of Multilabel Classifications with Perceptrons, IJCNN 2008 -
General extension of pairwise classification by Calibration in order to
divide the predicted ranking into relevant and irrelevant labels:
, , and , Multilabel Classification via Calibrated Label Ranking, Machine Learning Journal, 2008 -
Dual reformulation of MLPP in order to deal with a large number of
labels (up to 4000) though quadratic number of base classifiers,
introduction of the EUR-Lex dataset:
and , Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain, Semantic Processing of Legal Texts, 2010 -
Enhancement of the Calibrated Label Ranking approach by the efficient
voting strategy QWeighted that reduces the predictive costs from
quadratic to n log n:
, and , Efficient Voting Prediction for Pairwise Multilabel Classification, Neurocomputing, 2010 -
Usage of XML-specific features and machine learning techniques for
information extraction applied to documents from the French IPR Law:
, Segmentation of legal documents, ICAIL 2009 - Connection between multi-task learning and multilabel classification in order to exploit label dependencies:
, Multilabel Classification in Parallel Tasks, Workshop at ICML 2010 - Application
of Subgroup Discovery finding locally exceptional patterns in
multilabel data in order to exploit label dependencies:
, , and , Multi-label LeGo -- Enhancing Multi-label Classifiers with Local Patterns, IDA11, 2012, longer TR here - Dissertation about (m)any aspect(s) of Efficient Pairwise Multilabel Classification, and more:
, Efficient Pairwise Multilabel Classification, 2012 - Learning of Multilabel Rules as a natural way of representing dependencies
, , Stacking Label Features for Learning Multilabel Rules, DS 14, 2014
and , Learning rules for multi-label classification: a stacking and a separate-and-conquer approach (2016), in: Machine Learning, 105:1(77--126) - Use of neural networks and techniques from deep learning for large scale text classification
, , , , , Large-Scale Multi-label Text Classification - Revisiting Neural Networks, ECML 14, 2014 - Solutions for tasks when objects may belong to labels with a certain grade, e.g. with 0 to 5 stars mapping of movies to genres
, , , Graded Multilabel Classification by Pairwise Comparisons, ICDM 14, 2014 - Exploiting hierarchies and joint embedding for (zero-shot) multilabel classification
, , Hyunwoo J. Kim, , Predicting Unseen Labels using Label Hierarchies in Large-Scale Multi-label Learning, ECML 15, 2015 - Multilabel-Classification of tweets
, and , A Rapid-Prototyping Framework for Extracting Small-Scale Incident-Related Information in Microblogs: Application of Multi-Label Classification on Tweets (2016), in: Information Systems, 57(88-110) - Link between multilabel classification and unsupervised
learning: Learning domain-depending embeddings with the help of a
labelled background corpus
, and , Medical Concept Embeddings via Labeled Background Corpora, in: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pages 4629--4636, European Language Resources Association (ELRA), 2016 - Using preference learning for dealing with importance in Automatic Text Summarization
, and , Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization, in: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany, pages 84-94, Association for Computational Linguistics, 2016 - Predicting positive labels one by one - applying sequence learning to the idea of classifier chains
, , and , Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification, in: Advances in Neural Information Processing Systems 30 (NIPS-17), 2017 - Extraction of rules from (deep) neural networks in order to enhance understandability
, and , DeepRED -- Rule Extraction from Deep Neural Networks, in: Discovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19--21, 2016, Proceedings, pages 457--473, Springer International Publishing, 2016
, and , Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction, in: Proceedings of the 20th International Conference on Discovery Science (DS-17), Springer-Verlag, 2017
For further publications and supervised student theses see the full list of publications and our publications site, or my Google scholar profile.
- Material
- Tutorial on Multilabel Classification given for WeRC/LKE/KDSL
- slides of first part, programming in MULAN, small programming project sources
- EUR-Lex Dataset
- Datasets for Graded Multilabel Classification
- Incident-Related Twitter Datasets
- Medical Concept Embeddings
- Other Involvements
- ALIS Project
- LPCforSOS
- GLocSyn
- AIPHES
- P³oodle - A browser extension/add-on for personalized privacy-protected web search
- and Stuff
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