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Iñaki Inza
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2020 – today
- 2025
- [j43]Mario Martínez-García
, Susana García-Gutierrez, Lasai Barreñada
Extending the learning using privileged information paradigm to logistic regression. Neurocomputing 615: 128869 (2025) - 2023
- [j42]Ander Carreño
SNDProb: A Probabilistic Approach for Streaming Novelty Detection. IEEE Trans. Knowl. Data Eng. 35(6): 6335-6348 (2023) - [c17]Roberto Santana, Irati Prol-Godoy, Ana Picallo-Pérez, Iñaki Inza:
Evolved Neural Networks for Building Energy Prediction. SSCI 2023: 801-806 - 2022
- [c16]Mario Martínez-García
Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization. AIME 2022: 332-342 - [c15]Amaia Arregi
Vibration Analysis for Rotatory Elements Wear Detection in Paper Mill Machine. DEXA Workshops 2022: 199-209 - 2020
- [j41]Ander Carreño
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework. Artif. Intell. Rev. 53(5): 3575-3594 (2020) - [c14]Matija Soric, Danijela Pongrac, Iñaki Inza
Using Convolutional Neural Network for Chest X-ray Image classification. MIPRO 2020: 1771-1776
2010 – 2019
- 2019
- [j40]Jerónimo Hernández-González
Aggregated outputs by linear models: An application on marine litter beaching prediction. Inf. Sci. 481: 381-393 (2019) - [j39]Jerónimo Hernández-González
A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators. IEEE Trans. Knowl. Data Eng. 31(1): 195-200 (2019) - 2018
- [j38]Jerónimo Hernández-González
Learning to classify software defects from crowds: A novel approach. Appl. Soft Comput. 62: 579-591 (2018) - [j37]Pablo Rozas Larraondo
A system for airport weather forecasting based on circular regression trees. Environ. Model. Softw. 100: 24-32 (2018) - 2017
- [j36]Jerónimo Hernández-González
Learning from Proportions of Positive and Unlabeled Examples. Int. J. Intell. Syst. 32(2): 109-133 (2017) - [j35]Jonathan Ortigosa-Hernández, Iñaki Inza
Measuring the class-imbalance extent of multi-class problems. Pattern Recognit. Lett. 98: 32-38 (2017) - 2016
- [j34]Aritz Pérez
Efficient approximation of probability distributions with k-order decomposable models. Int. J. Approx. Reason. 74: 58-87 (2016) - [j33]Jerónimo Hernández-González
Weak supervision and other non-standard classification problems: A taxonomy. Pattern Recognit. Lett. 69: 49-55 (2016) - [j32]Jonathan Ortigosa-Hernández, Iñaki Inza
Semisupervised Multiclass Classification Problems With Scarcity of Labeled Data: A Theoretical Study. IEEE Trans. Neural Networks Learn. Syst. 27(12): 2602-2614 (2016) - 2015
- [j31]Guzmán Santafé, Iñaki Inza
Dealing with the evaluation of supervised classification algorithms. Artif. Intell. Rev. 44(4): 467-508 (2015) - [j30]Jose A. Fernandes
Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecol. Informatics 25: 35-42 (2015) - [j29]Jerónimo Hernández-González
Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection. Int. J. Intell. Syst. 30(3): 326-354 (2015) - [c13]Jerónimo Hernández-González
A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions. CAEPIA 2015: 3-13 - 2014
- [j28]Ramón Sagarna, Alexander Mendiburu
Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing. Inf. Sci. 258: 122-139 (2014) - 2013
- [j27]Jose Luis Flores
A new measure for gene expression biclustering based on non-parametric correlation. Comput. Methods Programs Biomed. 112(3): 367-397 (2013) - [j26]Jose A. Fernandes
Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting. Environ. Model. Softw. 40: 245-254 (2013) - [j25]Jerónimo Hernández-González
Learning Bayesian network classifiers from label proportions. Pattern Recognit. 46(12): 3425-3440 (2013) - [c12]Jerónimo Hernández-González
Learning from Crowds in Multi-dimensional Classification Domains. CAEPIA 2013: 352-362 - [c11]Aritz Pérez Martínez, José Antonio Lozano, Iñaki Inza
Multidimensional k-Interaction Classifier: Taking Advantage of All the Information Contained in Low Order Interactions. CAEPIA 2013: 393-401 - 2012
- [j24]Jonathan Ortigosa-Hernández, Juan Diego Rodríguez, Leandro Alzate, Manuel Lucania, Iñaki Inza
Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92: 98-115 (2012) - [j23]Borja Calvo
Wrapper positive Bayesian network classifiers. Knowl. Inf. Syst. 33(3): 631-654 (2012) - 2011
- [j22]Rubén Armañanzas
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. IEEE ACM Trans. Comput. Biol. Bioinform. 8(3): 760-774 (2011) - [c10]Jerónimo Hernández-González
Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions. CAEPIA 2011: 134-144
2000 – 2009
- 2009
- [j21]Aritz Pérez Martínez
Bayesian classifiers based on kernel density estimation: Flexible classifiers. Int. J. Approx. Reason. 50(2): 341-362 (2009) - [j20]Rubén Armañanzas
Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures. IEEE Trans. Inf. Technol. Biomed. 13(3): 341-350 (2009) - 2008
- [j19]Rubén Armañanzas
A review of estimation of distribution algorithms in bioinformatics. BioData Min. 1 (2008) - [j18]Rubén Armañanzas
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers. Comput. Methods Programs Biomed. 91(2): 110-121 (2008) - [c9]Yvan Saeys, Huan Liu, Iñaki Inza, Louis Wehenkel, Yves Van de Peer:
Preface. FSDM 2008: 1-4 - [e2]Yvan Saeys, Huan Liu, Iñaki Inza, Louis Wehenkel, Yves Van de Peer:
Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery, FSDM 2008, held at ECML-PKDD 2008, Antwerp, Belgium, September 15, 2008. JMLR Proceedings 4, JMLR.org 2008 [contents] - 2007
- [j17]Yvan Saeys
A review of feature selection techniques in bioinformatics. Bioinform. 23(19): 2507-2517 (2007) - [j16]Jose Luis Flores, Iñaki Inza, Pedro Larrañaga:
Wrapper discretization by means of estimation of distribution algorithms. Intell. Data Anal. 11(5): 525-545 (2007) - 2006
- [j15]Pedro Larrañaga
Machine learning in bioinformatics. Briefings Bioinform. 7(1): 86-112 (2006) - [j14]Aritz Pérez Martínez
Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes. Int. J. Approx. Reason. 43(1): 1-25 (2006) - [c8]Aritz Pérez Martínez, Pedro Larrañaga
Information Theory and Classification Error in Probabilistic Classifiers. Discovery Science 2006: 347-351 - [e1]José Antonio Lozano, Pedro Larrañaga, Iñaki Inza
Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms. Studies in Fuzziness and Soft Computing 192, Springer 2006, ISBN 978-3-540-29006-3 [contents] - 2005
- [j13]Rosa Blanco, Iñaki Inza
Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. J. Biomed. Informatics 38(5): 376-388 (2005) - [j12]Pedro Larrañaga
Editorial. Mach. Learn. 59(3): 211-212 (2005) - [p4]Pedro Larrañaga
A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models. Data Analysis and Visualization in Genomics and Proteomics 2005: 215-238 - 2004
- [j11]Iñaki Inza
Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Medicine 31(2): 91-103 (2004) - [j10]Rosa Blanco, Pedro Larrañaga
Gene Selection For Cancer Classification Using Wrapper Approaches. Int. J. Pattern Recognit. Artif. Intell. 18(8): 1373-1390 (2004) - [c7]Rosa Blanco, Linda C. van der Gaag, Iñaki Inza
Selective Classifiers Can Be Too Restrictive: A Case-Study in Oesophageal Cancer. ISBMDA 2004: 212-223 - 2003
- [j9]Rosa Blanco, Iñaki Inza
Learning Bayesian networks in the space of structures by estimation of distribution algorithms. Int. J. Intell. Syst. 18(2): 205-220 (2003) - 2002
- [j8]Iñaki Inza, Basilio Sierra, Rosa Blanco:
Gene selection by sequential search wrapper approaches in microarray cancer class prediction. J. Intell. Fuzzy Syst. 12(1): 25-33 (2002) - [c6]Rosa Blanco, Iñaki Inza, Pedro Larrañaga:
Floating Search Methods in Learning Bayesian Networks. Probabilistic Graphical Models 2002 - [p3]Iñaki Inza
Feature Subset Selection by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 269-293 - [p2]Iñaki Inza
Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 295-311 - [p1]Basilio Sierra
Rule Induction by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 313-322 - 2001
- [j7]Basilio Sierra
Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. Artif. Intell. Medicine 22(3): 233-248 (2001) - [j6]Iñaki Inza
Feature subset selection by genetic algorithms and estimation of distribution algorithms - A case study in the survival of cirrhotic patients treated with TIPS. Artif. Intell. Medicine 23(2): 187-205 (2001) - [j5]Iñaki Inza
Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms. Int. J. Approx. Reason. 27(2): 143-164 (2001) - [j4]José M. Peña, José Antonio Lozano, Pedro Larrañaga
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Trans. Pattern Anal. Mach. Intell. 23(6): 590-603 (2001) - [c5]Basilio Sierra
Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS. AIME 2001: 20-29 - [c4]Basilio Sierra
On Applying Supervised Classification Techniques in Medicine. ISMDA 2001: 14-19 - 2000
- [j3]Iñaki Inza
Feature Subset Selection by Bayesian network-based optimization. Artif. Intell. 123(1-2): 157-184 (2000) - [c3]Basilio Sierra
Medical Bayes Networks. ISMDA 2000: 4-14 - [c2]Iñaki Inza
Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS. ISMDA 2000: 97-110
1990 – 1999
- 1999
- [j2]Pedro Larrañaga
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. Artif. Intell. Rev. 13(2): 129-170 (1999) - [j1]Iñaki Inza
Representing the behaviour of supervised classification learning algorithms by Bayesian networks. Pattern Recognit. Lett. 20(11-13): 1201-1209 (1999) - [c1]Basilio Sierra
Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit. AIMDM 1999: 366-371
Coauthor Index
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last updated on 2024-12-02 22:35 CET by the dblp team
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