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ORCIDEneldo Loza Mencía
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- affiliation: TU Darmstadt, Department of Computer Science, Germany
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2020 – today
- 2023
[j10]Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender, Johannes Fürnkranz
:
Tree-based dynamic classifier chains. Mach. Learn. 112(11): 4129-4165 (2023)- [j9]Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa López, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Vakaj, Gerard de Melo:
Knowledge Graph Embeddings: Open Challenges and Opportunities. TGDK 1(1): 4:1-4:32 (2023) - 2022
- [j8]Antonella Plaia, Simona Buscemi, Johannes Fürnkranz, Eneldo Loza Mencía:
Comparing Boosting and Bagging for Decision Trees of Rankings. J. Classif. 39(1): 78-99 (2022) - [j7]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A flexible class of dependence-aware multi-label loss functions. Mach. Learn. 111(2): 713-737 (2022) - [i18]Florian Busch, Moritz Kulessa, Eneldo Loza Mencía, Hendrik Blockeel:
Combining Predictions under Uncertainty: The Case of Random Decision Trees. CoRR abs/2208.07403 (2022) - 2021
- [j6]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance. Comput. 10(3): 32 (2021) - [j5]Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance. Frontiers Big Data 4: 784159 (2021) - [c46]Moritz Kulessa, Bennet Wittelsbach, Eneldo Loza Mencía, Johannes Fürnkranz:
Sum-Product Networks for Early Outbreak Detection of Emerging Diseases. AIME 2021: 61-71 - [c45]Florian Busch, Moritz Kulessa, Eneldo Loza Mencía, Hendrik Blockeel:
Combining Predictions Under Uncertainty: The Case of Random Decision Trees. DS 2021: 78-93 - [c44]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Revisiting Non-specific Syndromic Surveillance. IDA 2021: 128-140 - [c43]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-Based Label Binning in Multi-label Classification. ECML/PKDD (3) 2021: 462-477 - [i17]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Revisiting Non-Specific Syndromic Surveillance. CoRR abs/2101.12246 (2021) - [i16]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-based Label Binning in Multi-label Classification. CoRR abs/2106.11690 (2021) - [i15]Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance. CoRR abs/2110.09208 (2021) - [i14]Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender, Johannes Fürnkranz:
Tree-Based Dynamic Classifier Chains. CoRR abs/2112.06672 (2021) - 2020
- [c42]Simon Bohlender, Eneldo Loza Mencía, Moritz Kulessa:
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains. DS 2020: 471-485 - [c41]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. DS 2020: 533-547 - [c40]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. KI 2020: 290-296 - [c39]Margot Mieskes, Eneldo Loza Mencía, Tim Kronsbein:
A Data Set for the Analysis of Text Quality Dimensions in Summarization Evaluation. LREC 2020: 6690-6699 - [c38]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. ECML/PKDD (3) 2020: 124-140 - [c37]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, Michael Rapp:
Rule-Based Multi-label Classification: Challenges and Opportunities. RuleML+RR 2020: 3-19 - [i13]Simon Bohlender, Eneldo Loza Mencía, Moritz Kulessa:
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains. CoRR abs/2006.08094 (2020) - [i12]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. CoRR abs/2006.11916 (2020) - [i11]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. CoRR abs/2006.13346 (2020) - [i10]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. CoRR abs/2007.08145 (2020) - [i9]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A Flexible Class of Dependence-aware Multi-Label Loss Functions. CoRR abs/2011.00792 (2020) - [i8]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Michael Rapp:
Learning Structured Declarative Rule Sets - A Challenge for Deep Discrete Learning. CoRR abs/2012.04377 (2020)
2010 – 2019
- 2019
- [c36]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Improving the Fusion of Outbreak Detection Methods with Supervised Learning. CIBB 2019: 55-66 - [c35]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics. DS 2019: 96-111 - [c34]Yannik Klein, Michael Rapp, Eneldo Loza Mencía:
Efficient Discovery of Expressive Multi-label Rules Using Relaxed Pruning. DS 2019: 367-382 - [c33]Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz:
Learning Context-dependent Label Permutations for Multi-label Classification. ICML 2019: 4733-4742 - [i7]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods. CoRR abs/1907.07464 (2019) - [i6]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics. CoRR abs/1908.03032 (2019) - [i5]Yannik Klein, Michael Rapp, Eneldo Loza Mencía:
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning. CoRR abs/1908.06874 (2019) - [i4]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy. CoRR abs/1911.04393 (2019) - 2018
- [c32]Moritz Kulessa, Eneldo Loza Mencía:
Dynamic Classifier Chain with Random Decision Trees. DS 2018: 33-50 - [c31]Patryk Hopner, Eneldo Loza Mencía:
Analysis and Optimization of Deep Counterfactual Value Networks. KI 2018: 305-312 - [c30]Markus Zopf, Eneldo Loza Mencía, Johannes Fürnkranz:
Which Scores to Predict in Sentence Regression for Text Summarization? NAACL-HLT 2018: 1782-1791 - [c29]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules. PAKDD (1) 2018: 29-42 - [c28]Markus Zopf, Teresa Botschen, Tobias Falke, Benjamin Heinzerling, Ana Marasovic, Todor Mihaylov, Avinesh P. V. S., Eneldo Loza Mencía, Johannes Fürnkranz, Anette Frank:
What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization. SNAMS 2018: 272-277 - [i3]Eneldo Loza Mencía, Patryk Hopner:
Analysis and Optimization of Deep CounterfactualValue Networks. CoRR abs/1807.00900 (2018) - [i2]Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp:
Learning Interpretable Rules for Multi-label Classification. CoRR abs/1812.00050 (2018) - [i1]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules. CoRR abs/1812.06833 (2018) - 2017
- [c27]Camila González, Eneldo Loza Mencía, Johannes Fürnkranz:
Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction. DS 2017: 127-143 - [c26]Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz:
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification. NIPS 2017: 5413-5423 - [c25]Mohammed Arif Khan, Asif Ekbal, Eneldo Loza Mencía, Johannes Fürnkranz:
Multi-objective Optimisation-Based Feature Selection for Multi-label Classification. NLDB 2017: 38-41 - 2016
- [j4]Axel Schulz, Eneldo Loza Mencía, Benedikt Schmidt:
A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: Application of multi-label classification on tweets. Inf. Syst. 57: 88-110 (2016) - [j3]Eneldo Loza Mencía, Frederik Janssen:
Learning rules for multi-label classification: a stacking and a separate-and-conquer approach. Mach. Learn. 105(1): 77-126 (2016) - [c24]Jinseok Nam, Eneldo Loza Mencía, Johannes Fürnkranz:
All-in Text: Learning Document, Label, and Word Representations Jointly. AAAI 2016: 1948-1954 - [c23]Markus Zopf, Eneldo Loza Mencía, Johannes Fürnkranz:
Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization. COLING 2016: 1071-1082 - [c22]Markus Zopf, Eneldo Loza Mencía, Johannes Fürnkranz:
Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization. CoNLL 2016: 84-94 - [c21]Jan Ruben Zilke, Eneldo Loza Mencía, Frederik Janssen:
DeepRED - Rule Extraction from Deep Neural Networks. DS 2016: 457-473 - [c20]Prateek Veeranna Sappadla, Jinseok Nam, Eneldo Loza Mencía, Johannes Fürnkranz:
Using semantic similarity for multi-label zero-shot classification of text documents. ESANN 2016 - [c19]Eneldo Loza Mencía, Gerard de Melo, Jinseok Nam:
Medical Concept Embeddings via Labeled Background Corpora. LREC 2016 - 2015
- [c18]Mohammed Arif Khan, Asif Ekbal, Eneldo Loza Mencía:
Simultaneous Feature Selection and Parameter Optimization Using Multi-objective Optimization for Sentiment Analysis. ICON 2015: 285-294 - [c17]Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz:
Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning. ECML/PKDD (1) 2015: 102-118 - 2014
- [c16]Eneldo Loza Mencía, Frederik Janssen:
Stacking Label Features for Learning Multilabel Rules. Discovery Science 2014: 192-203 - [c15]Petar Ristoski, Eneldo Loza Mencía, Heiko Paulheim:
A Hybrid Multi-strategy Recommender System Using Linked Open Data. SemWebEval@ESWC 2014: 150-156 - [c14]Christian Brinker, Eneldo Loza Mencía, Johannes Fürnkranz:
Graded Multilabel Classification by Pairwise Comparisons. ICDM 2014: 731-736 - [c13]Axel Schulz, Eneldo Loza Mencía, Thanh-Tung Dang, Benedikt Schmidt:
Evaluating Multi-label Classification of Incident-related Tweet. #MSM 2014: 26-33 - [c12]Jinseok Nam, Jungi Kim, Eneldo Loza Mencía, Iryna Gurevych, Johannes Fürnkranz:
Large-Scale Multi-label Text Classification - Revisiting Neural Networks. ECML/PKDD (2) 2014: 437-452 - 2013
- [b1]Eneldo Loza Mencía:
Efficient pairwise multilabel classification. Darmstadt University of Technology, Germany, 2013, pp. 1-247 - [c11]Eneldo Loza Mencía, Simon Holthausen, Axel Schulz, Frederik Janssen:
Using Data Mining on Linked Open Data for Analyzing E-Procurement Information - A Machine Learning approach to the Linked Data Mining Challenge 2013. DMoLD 2013 - [c10]Eneldo Loza Mencía, Frederik Janssen:
Towards Multilabel Rule Learning. LWA 2013: 144-147 - 2012
- [c9]Wouter Duivesteijn, Eneldo Loza Mencía, Johannes Fürnkranz, Arno J. Knobbe:
Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. IDA 2012: 114-125 - 2010
- [j2]Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz:
Efficient voting prediction for pairwise multilabel classification. Neurocomputing 73(7-9): 1164-1176 (2010) - [c8]Eneldo Loza Mencía, Johannes Fürnkranz:
Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. Semantic Processing of Legal Texts 2010: 192-215 - [c7]Eneldo Loza Mencía:
An Evaluation of Multilabel Classification for the Automatic Annotation of Texts. LWA 2010: 121-123
2000 – 2009
- 2009
- [c6]Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz:
Efficient voting prediction for pairwise multilabel classification. ESANN 2009 - [c5]Eneldo Loza Mencía:
Segmentation of legal documents. ICAIL 2009: 88-97 - [c4]Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz:
Efficient Voting Prediction for Pairwise Multilabel Classification. LWA 2009: KDML:72-75 - 2008
- [j1]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker:
Multilabel classification via calibrated label ranking. Mach. Learn. 73(2): 133-153 (2008) - [c3]Eneldo Loza Mencía, Johannes Fürnkranz:
Pairwise learning of multilabel classifications with perceptrons. IJCNN 2008: 2899-2906 - [c2]Eneldo Loza Mencía, Johannes Fürnkranz:
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain. ECML/PKDD (2) 2008: 50-65 - 2007
- [c1]Eneldo Loza Mencía, Johannes Fürnkranz:
An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. LWA 2007: 126-132
Coauthor Index
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