Keywords:
- Combine then Predict
- deep neural networks
- efficiency
- efficient classification
- Ensembles of Multilabel Classifiers
- EUR-Lex Database
- F-measure
- Fusion
- Game Abstractions
- Gradient boosting
- Hamming loss
- heuristics
- Label Dependencies
- learning by pairwise comparison
- Legal Documents
- Microblogs
- multi-label learning
- multilabel classification
- Outbreak Detection
- pairwise classification
- poker
- Predict then Combine
- Rule Learning
- scalability
- separate- and-conquer
- Social Media
- Stacking
- Subset 0/1 loss
- Surveillance
- Text Classification
- voting aggregation
Publications of Eneldo Loza Mencía sorted by first author
A
| , , and , Auswertung der Ersteinschätzung-Routine-Daten unter Verwendung von statistischen Analyseverfahren zur Früherkennung von epidemischen Gefahrenlagen, in: 15. Jahrestagung Deutsche Gesellschaft Interdisziplinäre Notfall- und Akutmedizin (DGINA), 2020 |
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| , , , and , Evaluation der im Rahmen des ESEG-Projektes implementierten Pflichtelemente für ein EDV-gestütztes Ersteinschätzungssystem in interdisziplinären zentralen Notaufnahmen, in: 15. Jahrestagung Deutsche Gesellschaft Interdisziplinäre Notfall- und Akutmedizin (DGINA), 2020 |
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B
| , and , Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, Knowledge Engineering Group, Technische Universität Darmstadt, number 2006.08094 [cs.LG], ArXiv e-prints, 2020 |
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| , 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 |
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| , and , Graded Multilabel Classification by Pairwise Comparisons, in: 2014 IEEE International Conference on Data Mining (ICDM 2014), pages 731--736, Curran Associates, IEEE, 2014 |
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| , and , Graded Multilabel Classification by Pairwise Comparisons, Knowledge Engineering Group, Technische Universität Darmstadt, Technical Report, 2014 |
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| , , and , Comparing Boosting and Bagging for Decision Trees of Rankings (2021), in: Journal of Classification |
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| , , and , Combining Predictions under Uncertainty: The Case of Random Decision Trees, in: Discovery Science, pages 78--93, Springer, 2021 |
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D
| , , and , PLAY: A Profiled Linear Weighting Scheme for Understanding the Influence of Input Variables on the Output of a Deep Artificial Neural Network (2020), in: Archives of Data Science, Series A |
| , , and , Multi-label LeGo -- Enhancing Multi-label Classifiers with Local Patterns, Knowledge Engineering Group, Technische Universität Darmstadt, number TUD-KE-2012-02, 2012 |
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| , , and , Multi-label LeGo -- Enhancing Multi-label Classifiers with Local Patterns, in: Advances in Intelligent Data Analysis XI -- Proceedings of the 11th International Symposium on Data Analysis (IDA-11), Berlin, pages 114--125, Springer, 2012 |
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F
| , , and , Multilabel Classification via Calibrated Label Ranking (2008), in: Machine Learning, 73:2(133--153) |
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| , , and , Learning Structured Declarative Rule Sets — A Challenge for Deep Discrete Learning, in: 2nd Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), 2020 |
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| , and , Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction, in: Proceedings of the 20th International Conference on Discovery Science (DS-17), pages 127--143, Springer-Verlag, 2017 |
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| and , Analysis and Optimization of Deep Counterfactual Value Networks, Knowledge Engineering Group, Technische Universität Darmstadt, number 1807.00900, 2018 |
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| and , Analysis and Optimization of Deep Counterfactual Value Networks, in: KI 2018: Advances in Artificial Intelligence, pages 305--312, Springer International Publishing, 2018 |
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| , , , , , , and , Risikostratifizierung durch Implementierung und Evaluation eines COVID-19-Scores (2020), in: Medizinische Klinik - Intensivmedizin und Notfallmedizin, 115(132–-138) |
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| , , , and , Rule-Based Multi-label Classification: Challenges and Opportunities, in: Rules and Reasoning, pages 3--19, Springer International Publishing, 2020 |
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| , and , Conformal Rule-Based Multi-label Classification, in: KI 2020: Advances in Artificial Intelligence, Springer, Cham, 2020 |
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| , , , and , A Flexible Class of Dependence-sensitive Multi-label Loss Functions (2021), in: Machine Learning Journal |
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| , and , Simultaneous Feature Selection and Parameter Optimization Using Multi-objective Optimization for Sentiment Analysis, in: Proceedings of the 12th International Conference on Natural Language Processing (ICON), Trivandrum, India, pages 285--294, NLP Association of India, 2015 |
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| , , and , Multi-Objective Optimisation-based Feature Selection for Multi-Label Classification, Knowledge Engineeering Group, TU Darmstadt, number TUD-KE-2017-01, 2017 |
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| , , and , Multi-Objective Optimisation-based Feature Selection for Multi-Label Classification, in: Proceedings of the 22nd International Conference on Natural Language and Information Systems (NLDB-17), pages 38--41, Springer-Verlag, 2017 |
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| , and , Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning, in: Discovery Science, pages 367--382, Springer International Publishing, 2019 |
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| 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 |
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| , and , Revisiting Non-Specific Syndromic Surveillance (2021), in: CoRR, abs/2101.12246 |
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| , 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 |
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| , and , Improving Outbreak Detection with Stacking of Statistical Surveillance Methods, in: Workshop Proceedings of epiDAMIK: Epidemiology meets Data Mining and Knowledge discovery (held in conjunction with ACM SIGKDD 2019), Anchorage, USA, 2019 |
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| , 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 |
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| , , and , Sum-Product Networks for Early Outbreak Detection of Emerging Diseases, in: Artificial Intelligence in Medicine, pages 61--71, Springer International Publishing, 2021 |
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| , Efficient Pairwise Multilabel Classification, Technische Universität Darmstadt, Knowledge Engineering Group, 2012 |
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| , Segmentation of legal documents, in: Proceedings of the 12th International Conference on Artificial Intelligence and Law, Barcelona, Spain, pages 88--97, ACM, 2009 |
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| , Paarweises Lernen von Multilabel-Klassifikationen mit dem Perzeptron-Algorithmus, TU Darmstadt, Knowledge Engineering Group, 2006 |
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| , An Evaluation of Multilabel Classification for the Automatic Annotation of Texts, in: Proceedings of the LWA 2010: Lernen, Wissen, Adaptivität, Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML 2010), Kassel, pages 121-123, 2010 |
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| , 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 |
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| and , Pairwise Learning of Multilabel Classifications with Perceptrons, in: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08), IEEE, pages 2900--2907, 2008 |
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| and , Pairwise Learning of Multilabel Classifications with Perceptrons, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2007-05, 2007 |
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| and , Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Disocvery in Databases (ECML-PKDD-2008), Part II, pages 50--65, Springer, 2008 |
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| and , Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain, in: Semantic Processing of Legal Texts -- Where the Language of Law Meets the Law of Language, pages 192-215, Springer-Verlag, 2010 |
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| and , An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain, in: Proceedings of the LWA 2007: Lernen - Wissen - Adaption, pages 126--132, 2007 |
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| , , 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 |
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| and , An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain, in: Proceedings of the LREC 2008 Workshop on Semantic Processing of Legal Texts, pages 23-32, 2008 |
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| , , and , Using Data Mining on Linked Open Data for Analyzing E-Procurement Information, in: Proceedings of the first DMoLD: Data Mining on Linked Data Workshop at ECML/PKDD2013, 2013 |
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| and , Learning rules for multi-label classification: a stacking and a separate-and-conquer approach (2016), in: Machine Learning, 105:1(77--126) |
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| and , Stacking Label Features for Learning Multilabel Rules, in: Discovery Science - 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings, pages 192-203, Springer, 2014 |
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| and , Towards Multilabel Rule Learning, in: Proceedings of the German Workshop on Lernen, Wissen, Adaptivität - LWA2013, pages 155--158, 2013 |
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| , , and , Tree-Based Dynamic Classifier Chains (2021), in: Machine Learning Journal |
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| , and , Learning multi-labeled bioacoustic samples with an unsupervised feature learning approach, in: Proceedings of Neural Information Scaled for Bioacoustics, from Neurons to Big Data, NIPS Int. Conf., pages 184-189, 2013 |
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| , and , Efficient Voting Prediction for Pairwise Multilabel Classification (2010), in: Neurocomputing, 73:7-9(1164 - 1176) |
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| , and , Efficient Voting Prediction for Pairwise Multilabel Classification, in: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 2009, Bruges, Belgium), pages 117--122, d-side publications, 2009 |
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| , and , Advances in Efficient Pairwise Multilabel Classification, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2008-06, 2008 |
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| , and , Efficient Voting Prediction for Pairwise Multilabel Classification, in: Proceedings of the LWA 2009: Lernen - Wissen - Adaption, Workshop Knowledge Discovery, Data Mining and Machine Learning (KDML-09), Darmstadt, Germany, pages 72--75, 2009 |
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| , Multilabel Classification in Parallel Tasks, in: Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010, Haifa, Israel, pages 29-36, 2010 |
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| , 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 |
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| , , , and , Large-Scale Multi-label Text Classification - Revisiting Neural Networks, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-14), Part 2, pages 437--452, Springer Berlin Heidelberg, 2014 |
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| , , , , 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, {PMLR}, 2019 |
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| , 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 |
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| , , and , Predicting Unseen Labels using Label Hierarchies in Large-Scale Multi-label Learning, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, pages 102-118, Springer International Publishing, 2015 |
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| , , and , Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification, in: Advances in Neural Information Processing Systems 30 (NIPS-17), pages 5419--5429, 2017 |
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| , , , and , On Aggregation in Ensembles of Multilabel Classifiers, in: Discovery Science, pages 533--547, Springer International Publishing, 2020 |
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R
| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: CoRR, abs/2110.09208 |
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| , and , On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics, Knowledge Engineering Group, Technische Universität Darmstadt, number 1908.03032, ArXiv e-prints, 2019 |
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| , and , Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy, Knowledge Engineering Group, Technische Universität Darmstadt, number 1911.04393, ArXiv e-prints, 2019 |
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| , , and , Gradient-Based Label Binning in Multi-Label Classification, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer, 2021 |
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| , and , On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics, in: Discovery Science, pages 96--111, Springer International Publishing, 2019 |
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| , , , and , Learning Gradient Boosted Multi-label Classification Rules, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pages 124--140, Springer, 2020 |
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| , 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 |
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| , and , A Hybrid Multi-Strategy Recommender System Using Linked Open Data, in: Semantic Web Evaluation Challenge, Proceedings (ESWC 2014), pages 150-156, Springer, 2014 |
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| , , and , Using Semantic Similarity for Multi-Label Zero-Shot Classification of Text Documents, in: Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-16), d-side publications, 2016 |
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| , , and , Evaluating Multi-label Classification of Incident-related Tweets, in: Proceedings, 4th Workshop on Making Sense of Microposts {(\#Microposts2014)} at WWW: Big things come in small packages, pages 26--33, 2014 |
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| , 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) |
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| , , , and , On the Combination of Two Decompositive Multi-Label Classification Methods, in: Proceedings of the ECML PKDD 2009 Workshop on Preference Learning (PL-09, Bled, Slovenia), pages 114--129, 2009 |
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| , 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 |
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| , , , , , , , , 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 |
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| , 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 |
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| , and , Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization, in: Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan, pages 1071-1082, The COLING 2016 Organizing Committee, 2016 |
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| , 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 |
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