Minimalistic Homepage of Eneldo Loza Mencía
Find the new permanent location of my home page at https://research.eneldo.net.
- Contact
- research@eneldo.net
- Research Interests
- Multilabel, Large-Scale, Pairwise and Text Classification, Rule Learning, Deep Learning, Understandable and Interpretative Models, Text Summarization, Web Mining, Automatic Detection of Disease Outbreaks
- A selection of publications and their clusters
- Dynamic classifier chains: predicting positive labels one by one with trees
, , and , Tree-Based Dynamic Classifier Chains (2021), in: Machine Learning Journal
Simon Bohlender, and , Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, in: Discovery Science - 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings, pages 471--485, Springer International Publishing, 2020
Moritz Kulessa and , Dynamic Classifier Chain with Random Decision Trees, in: Proceedings of the 21st International Conference of Discovery Science (DS-18), Limassol, Cyprus, pages 33--50, Springer-Verlag, 2018 - Dynamic classifier chains with deep neural networks: 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
, , , , and , Learning Context-dependent Label Permutations for Multi-label Classification, in: Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 4733--4742, 2019 - Induction of rule-like disease patterns on symptomes using clinical patient data from emergency departments and time series of reported cases
, , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: CoRR, abs/2110.09208, submitted to Frontiers in Big Data - Detection of outbreaks of known and unknown diseases with statistical methods and sum-product networks
, , and , Sum-Product Networks for Early Outbreak Detection of Emerging Diseases, in: Artificial Intelligence in Medicine, pages 61--71, Springer International Publishing, 2021
, and , Revisiting Non-Specific Syndromic Surveillance, in: Advances in Intelligent Data Analysis {XIX} - 19th International Symposium on Intelligent Data Analysis, {IDA} 2021, Porto, Portugal, April 26-28, 2021, Proceedings, pages 128-140, Springer International Publishing, 2021 - Detection of disease outbreak on time series of labelled reported cases with stacking
, and , Improving the Fusion of Outbreak Detection Methods with Supervised Learning, in: Computational Intelligence Methods for Bioinformatics and Biostatistics - 16th International Meeting, {CIBB} 2019, Bergamo, Italy, September 4-6, 2019, Revised Selected Papers, Bergamo, Italy, pages 55--66, Springer, 2020 - Learning multilabel rules with boosting: efficient and effective
, , and , Gradient-Based Label Binning in Multi-Label Classification, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer, 2021
, , , and , Learning Gradient Boosted Multi-label Classification Rules, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pages 124--140, Springer, 2020 - Investigating the connection between label dependencies and the losses which are optimized
, , , and , A Flexible Class of Dependence-sensitive Multi-label Loss Functions (2021), in: Machine Learning Journal
, , , and , On Aggregation in Ensembles of Multilabel Classifiers, in: Discovery Science, pages 533--547, Springer International Publishing, 2020 - Learning interpretable multilabel rules with separate-and-conquer and tricks to make it more efficient and more expressive, and , Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules, in: PAKDD 2018: Advances in Knowledge Discovery and Data Mining, pages 29--42, Springer International Publishing, 2018
- 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 - Instead of just averaging, how to combine the predictions of the individual trees in an ensemble of random decision trees if you know about the certainty of the predictions
, , and , Combining Predictions under Uncertainty: The Case of Random Decision Trees, in: Discovery Science, pages 78--93, Springer, 2021 - Analyzing what is important in texts, what is relevant for different text quality criteria, and which features are useful for automatically producing text summaries
, and , A Data Set for the Analysis of Text Quality Dimensions in Summarization Evaluation, in: Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020), pages 6690–-6699, European Language Resources Association, 2020
Markus Zopf, , , , , , , , and , What’s important in a text? An extensive evaluation of linguistic annotations for summarization, in: Proceedings of the 5th International Conference on Social Networks Analysis, Management and Security (SNAMS-18), Valencia, Spain, pages 272--277, 2018
, and , Which Scores to Predict in Sentence Regression for Text Summarization?, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018), pages 1782--1791, 2018 - 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 - 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 - Learning embeddings for documents, labels and their descriptions, and words together: better classification accuracy and enables zero-shot learning
, and , All-in Text: Learning Document, Label, and Word Representations Jointly, in: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Arizona, pages 1948--1954, AAAI Press, 2016 - 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) - 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 - 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 - 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 - Learning of interpretable Multilabel Rules as a natural way of representing dependencies
, , and , Learning Interpretable Rules for Multi-label Classification, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, pages 81--113, Springer-Verlag, 2018
Eneldo Loza Mencía and , Learning rules for multi-label classification: a stacking and a separate-and-conquer approach (2016), in: Machine Learning, 105:1(77--126)
Eneldo Loza Mencía, , Stacking Label Features for Learning Multilabel Rules, DS 14, 2014 - Dissertation about (m)any aspect(s) of Efficient Pairwise Multilabel Classification, and more:
, Efficient Pairwise Multilabel Classification, 2012 - 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 - Connection between multi-task learning and multilabel classification in order to exploit label dependencies:
, Multilabel Classification in Parallel Tasks, Workshop at ICML 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 -
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 -
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 -
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 - The effective Multilabel Pairwise Perceptrons (MLPP) algorithm on the large Reuters RCV1 dataset:
and , Pairwise Learning of Multilabel Classifications with Perceptrons, IJCNN 2008
For further publications and supervised student theses see the full list of publications and our publications site, or my dblp or Google scholar profile.
- Material
- Tutorial on Multilabel Classification given for WeRC/LKE/KDSL
- slides of first part, programming in MULAN, small programming project sources
- Course Material for Data Mining and Machine Learning: Techniques and Algorithms
+49 6151 16-21811
+49 6151 16-
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