Iñaki Inza

Orcid: 0000-0003-4674-1755

According to our database1, Iñaki Inza authored at least 64 papers between 1999 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Extending the learning using privileged information paradigm to logistic regression.
Neurocomputing, 2025

2023
SNDProb: A Probabilistic Approach for Streaming Novelty Detection.
IEEE Trans. Knowl. Data Eng., June, 2023

Evolved Neural Networks for Building Energy Prediction.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

2022
Vibration Analysis for Rotatory Elements Wear Detection in Paper Mill Machine.
Proceedings of the Database and Expert Systems Applications - DEXA 2022 Workshops, 2022

Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization.
Proceedings of the Artificial Intelligence in Medicine, 2022

2020
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework.
Artif. Intell. Rev., 2020

Using Convolutional Neural Network for Chest X-ray Image classification.
Proceedings of the 43rd International Convention on Information, 2020

2019
A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators.
IEEE Trans. Knowl. Data Eng., 2019

Aggregated outputs by linear models: An application on marine litter beaching prediction.
Inf. Sci., 2019

2018
A system for airport weather forecasting based on circular regression trees.
Environ. Model. Softw., 2018

Learning to classify software defects from crowds: A novel approach.
Appl. Soft Comput., 2018

2017
Measuring the class-imbalance extent of multi-class problems.
Pattern Recognit. Lett., 2017

Learning from Proportions of Positive and Unlabeled Examples.
Int. J. Intell. Syst., 2017

2016
Semisupervised Multiclass Classification Problems With Scarcity of Labeled Data: A Theoretical Study.
IEEE Trans. Neural Networks Learn. Syst., 2016

Weak supervision and other non-standard classification problems: A taxonomy.
Pattern Recognit. Lett., 2016

Efficient approximation of probability distributions with k-order decomposable models.
Int. J. Approx. Reason., 2016

2015
Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection.
Int. J. Intell. Syst., 2015

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species.
Ecol. Informatics, 2015

Dealing with the evaluation of supervised classification algorithms.
Artif. Intell. Rev., 2015

A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions.
Proceedings of the Advances in Artificial Intelligence, 2015

2014
Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing.
Inf. Sci., 2014

2013
Learning Bayesian network classifiers from label proportions.
Pattern Recognit., 2013

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting.
Environ. Model. Softw., 2013

A new measure for gene expression biclustering based on non-parametric correlation.
Comput. Methods Programs Biomed., 2013

Multidimensional k-Interaction Classifier: Taking Advantage of All the Information Contained in Low Order Interactions.
Proceedings of the Advances in Artificial Intelligence, 2013

Learning from Crowds in Multi-dimensional Classification Domains.
Proceedings of the Advances in Artificial Intelligence, 2013

2012
Wrapper positive Bayesian network classifiers.
Knowl. Inf. Syst., 2012

Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers.
Neurocomputing, 2012

2011
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms.
IEEE ACM Trans. Comput. Biol. Bioinform., 2011

Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions.
Proceedings of the Advances in Artificial Intelligence, 2011

2009
Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures.
IEEE Trans. Inf. Technol. Biomed., 2009

Bayesian classifiers based on kernel density estimation: Flexible classifiers.
Int. J. Approx. Reason., 2009

2008
Preface.
Proceedings of the Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery, 2008

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers.
Comput. Methods Programs Biomed., 2008

A review of estimation of distribution algorithms in bioinformatics.
BioData Min., 2008

2007
Wrapper discretization by means of estimation of distribution algorithms.
Intell. Data Anal., 2007

A review of feature selection techniques in bioinformatics.
Bioinform., 2007

2006
Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes.
Int. J. Approx. Reason., 2006

Machine learning in bioinformatics.
Briefings Bioinform., 2006

Information Theory and Classification Error in Probabilistic Classifiers.
Proceedings of the Discovery Science, 9th International Conference, 2006

2005
Editorial.
Mach. Learn., 2005

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS.
J. Biomed. Informatics, 2005

A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models.
Proceedings of the Data Analysis and Visualization in Genomics and Proteomics, 2005

2004
Gene Selection For Cancer Classification Using Wrapper Approaches.
Int. J. Pattern Recognit. Artif. Intell., 2004

Filter versus wrapper gene selection approaches in DNA microarray domains.
Artif. Intell. Medicine, 2004

Selective Classifiers Can Be Too Restrictive: A Case-Study in Oesophageal Cancer.
Proceedings of the Biological and Medical Data Analysis, 5th International Symposium, 2004

2003
Learning Bayesian networks in the space of structures by estimation of distribution algorithms.
Int. J. Intell. Syst., 2003

2002
Gene selection by sequential search wrapper approaches in microarray cancer class prediction.
J. Intell. Fuzzy Syst., 2002

Floating Search Methods in Learning Bayesian Networks.
Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002

Rule Induction by Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Feature Subset Selection by Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

2001
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2001

Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms.
Int. J. Approx. Reason., 2001

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.
Artif. Intell. Medicine, 2001

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, 2001

On Applying Supervised Classification Techniques in Medicine.
Proceedings of the Medical Data Analysis, Second International Symposium, 2001

Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS.
Proceedings of the Artificial Intelligence Medicine, 2001

2000
Feature Subset Selection by Bayesian network-based optimization.
Artif. Intell., 2000

Medical Bayes Networks.
Proceedings of the Medical Data Analysis, First International Symposium, 2000

Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS.
Proceedings of the Medical Data Analysis, First International Symposium, 2000

1999
Representing the behaviour of supervised classification learning algorithms by Bayesian networks.
Pattern Recognit. Lett., 1999

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators.
Artif. Intell. Rev., 1999

Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit.
Proceedings of the Artificial Intelligence in Medicine. Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, 1999


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