Iñaki Inza

I am member of the "Intelligent Systems Group" research team, which leader is Jose A. Lozano. I'm a associate professor at the "Intelligent Systems Group", of the University of the Basque Country. The research group is located in the Computer Science and AI Department, Computer Engineering Faculty, in Donostia - San Sebastián, Basque Country, north of Spain. My PhD advisor was Pedro Larrañaga. You can take a look to the web pages of my friends-colleagues Yvan Saeys, Joao Gama and Gavin Brown. I co-supervised the International-PhD thesis of Ander Carreño, Rubén Armañanzas, Jose A. Fernándes, Jerónimo Hernández, Jonathan Ortigosa, Pablo Rozas-Larraondo, Aritz Pérez and Rosa Blanco. I currently co-supervise the PhD project of Mario Martinez. post: Iñaki Inza Computer Science Faculty University of the Basque Country (Campus Ibaeta) Paseo Manuel de Lardizabal s/n 20018 Donostia - San Sebastián Basque Country, Spain email: inaki.inza at ehu.es phone:     +34 943 015026      fax:     +34 943 015590

Research interests

  - Machine Learning in general, specially for learning predictive models              
   - Non-standard classification scenarios with label uncertainty: weakly supervised classification, semi-supervised classification, learning from crowds...
   - Feature Subset Selection in Machine Learning and Data Mining (FSS)
   - Learning with streaming data

   - Applications in Biomedicine and Bioinformatics: microarrays, microRNAs and mass spectrometry data in diagnosis-prognosis-classification tasks -- biomarker identification
   - Applications in Weather Forecasting, Oceanographic and Ecological data  

   Google Scholar profile      ORCID ID profile

   You can find in the following webpage a compilation of data mining applications which attracted my attention.
    Although the used methodology is mentioned, they are written in a divulgative style, where emphasis is put on the problem solved.

• Refereed JCR-Impact factor Journals:
  
  
  • A. Carreño, I. Inza, J.A. Lozano (2023). SNDProb: a probabilistic approach for streaming novelty detection.  IEEE-TKDE Transacations on Knowledge and Data Engineering, 35(6), 6335-6348.[preprint version]
  • A.M. Ascensión, O. Ibáñez-Solé, I. Inza, A. Izeta, M.J. Araúzo-Bravo (2022). Triku: a feature selection method based on nearest neighbors for single-cell data.  GigaScience. 11, giac017.
    
  • P. Rozas-Larraondo, L.J. Renzullo, A.I.J.M. Van Dijk, I. Inza., J.A. Lozano (2020). Optimization of deep learning precipitation models using categorical binary metrics.  Journal of Advances in Modeling Earth Systems. 12(5), e2019MS001909.
    
  • A. Carreño, I. Inza, J.A. Lozano (2020). Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework. Artificial Intelligence Review, 53(5), 3575-3594.
  • J. Hernández-González, I. Inza, I. Granado, O.C. Basurko, J.A. Fernandes, J.A. Lozano (2019). Aggregated outputs by linear models: an application on marine litter beaching prediction. Information Sciences, 481, 381-393.
    
  • J. Hernández-González, I. Inza, J.A. Lozano (2019). A note on the behavior of majority voting in multi-class domains with biased annotators. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 31(1), 195-200.
  • J. Hernández-González, I. Inza, L. Crisol-Ortíz, M.A. Guembe, M.J. Iñarra, J.A. Lozano (2018). Fitting the data from embryo implantation prediction: learning from label proportions. Statistical Methods in Medical Research. 27(4), 1056-1066.
  • J. Hernández-González, D. Rodríguez, I. Inza, R. Harrison, J.A. Lozano (2018). Learning to classify software defects from crowds: a novel approach. Applied Soft Computing, 62, 579-591.
    
  • J. Hernández-González, D. Rodríguez, I. Inza, R. Harrison, J.A. Lozano (2018). Two datasets of defect reports labeled by a crowd of annotators of unknown reliability. Data in Brief, 18, 840-845.
    
  • P. Rozas-Larraondo, I. Inza, J.A. Lozano (2018). A system for airport weather forecasting based on circular regression trees. Environmental Modelling & Software, 100, 24-32.
    
  • J. Ortigosa-Hernández, I. Inza, J.A. Lozano (2017). Measuring the class-imbalance extent in multi-class problems. Pattern Recognition Letters, 98, 32-38.
  • J. Hernández-González, I. Inza, J.A. Lozano (2017). Learning from proportions of positive and unlabaled samples. International Journal of Intelligent Systems, 32(2), 109-133.
  • J. Ortigosa-Hernández, I. Inza, J.A. Lozano (2016). Semisupervised multiclass classification problems with scarcity of labeled data: a theoretical study. IEEE Neural Networks and Learning Systems, 27(12), 2602-2614.
  • J. Hernández-González, I. Inza, J.A. Lozano (2016). Weak supervision and other non-standard classification problems: A taxonomy. Pattern Recognition Letters, 69, 49-55. 
  • A. Pérez, I. Inza, J.A. Lozano (2016). Efficient approximation of probability distributions with k-order decomposable models. International Journal of Approximate Reasoning, 74, 58-87.
    
  • J. Hernández-González, I. Inza, J.A. Lozano (2015). Multi-dimensional learning from crowds: usefulness and application of expertise detection. International Journal of Intelligent Systems, 30(3), 326-354.  
  • G. Santafe, I. Inza, J.A. Lozano (2015). Dealing with the evaluation of supervised classification algorithms. Artificial Intelligence Review, 44(4), 467-508.
  • J.A. Fernandes, X. Irigoien, I. Inza, J.A. Lozano, N. Goikoetxea, A. Pérez (2015). Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecological Informatics, 25, 35-42.
    
  • P. Rozas-Larraondo, I. Inza, J.A. Lozano (2014). A method for wind speed forecasting in airports based on non-parametric regression. Weather and Forecasting, 29, 1332-1342.
    
  • R. Sagarna, A. Mendiburu, I. Inza, J.A. Lozano (2014). Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing. Information Sciences, 258, 122-139.
  • J. Hernández-González, I. Inza, J.A. Lozano (2013). Learning Bayesian network classifiers from label proportions. Pattern Recognition, 46(12), 3425-3440.
  • J.L. Flores, I. Inza, P. Larrañaga, B. Calvo (2013). A new measure for gene expression biclustering based on non-parametric correlation. Computer Methods and Programs in Biomedicine, 112(3), 367-397.
     
  • J.A. Fernandes, J.A. Lozano, I. Inza, X. Irigoien, A. Pérez, J.D. Rodríguez (2013). Supervised pre-processing approaches in multiple class variables classification for fish recruirment forecasting. Environmental Modelling & Software, 40, 245-254. 
  • B. Calvo, I. Inza, P. Larrañaga, J.A. Lozano (2012). Wrapper positive Bayesian network classifiers. Knowledge and Information Systems, 33(3), 631-654. 
  • J. Ortigosa-Hernández, J.D. Rodriguez, L. Alzate, M. Lucania I. Inza, J.A. Lozano (2012). Approaching Sentiment Analysis by Using Semi-supervised Learning of Multi-dimensional Classifiers. Special Issue in Data Mining Applicacions and Case Studies. Neurocomputing journal, 92, 98-115 
  • A. Garcia-Bilbao, R. Armañanzas, Z. Ispizua, B. Calvo, A. Alonso-Varona, I. Inza, P. Larrañaga, G. López-Vivanco, B. Suárez-Merino, M. Betanzos (2012). Identification of a biomarker panel for colorectal cancer diagnosis. BMC Cancer, 12:43. 
  • R. Armañanzas, Y. Saeys, I. Inza, M. García-Torres, C. Bielza, Y. van de Peer, P. Larrañaga (2011). Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(3), 760-774.
  • J.A. Fernandes, X. Irigoien, N. Goikoetxea, J.A. Lozano, I. Inza, A. Pérez, A. Bode (2010). Fish recruitment prediction, using robust supervised classification methods. Ecological Modelling, 221(2), 338-352.
  • A. Pérez, P. Larrañaga, I. Inza (2009). Bayesian classifiers based on kernel estimation: Flexible classifiers. International Journal of Approximate Reasoning, 50(2), 341-362.
  • R. Armañanzas, B. Calvo, I. Inza, M. López-Hoyos, V. Martínez-Taboada, E. Ucar, I. Bernales, A. Fullaondo, P. Larrañaga, A. M. Zubiaga (2009). Microarray analysis of autoimmune diseases by machine learning procedures. IEEE Transactions on Information Technology in Biomedicine, 13(3), 341-350.
  • J.A. Fernandes, X. Irigoien, J.A. Lozano, I. Inza (2009). Optimizing the number of classes in automated zooplankton classification. Journal of Plankton Research, 31(1), 19-29.
  • D. Otaegui, S.E. Baranzini, R. Armañanzas, B. Calvo, M. Muñoz-Culla, P. Khankhanian, I. Inza, J.A. Lozano, T. Castillo-Triviño, A. Asensio, J. Olaskoaga, A. López de Munain (2009). Differential micro RNA expression in PBMC from multiple sclerosis patients. PLoS ONE, 4(7), e6309.
  • A. Sáenz, M. Azpitarte, R. Armañanzas, F. Leturq, A. Alzualde, I. Inza, F. García-Bragado, G. De la Herran, J. Corcuera, A. Cabello, C. Navarro, C. De la Torre, E. Gallardo, I. Illa, A. López de Munain (2008). Gene expression profiling in Limb-Girdle Muscular Dystrophy 2A. PLoS ONE, 3(11), e3750.
  • R. Armañanzas, I. Inza, R. Santana, Y. Saeys, J.L. Flores, J.A. Lozano, Y. Van de Peer, R. Blanco, V. Robles, C. Bielza, P. Larrañaga (2008). A review of estimation of distribution algorithms in bioinformatics. BioDataMining, 1(6).
  • R. Armañanzas, I. Inza, P. Larrañaga (2008). Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers. Computer Methods and Programs in Biomedicine, 91(2), 110-121.
  • Co-organized event: ECML PKDD 2008 - Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery - FSDM08
    Accepted papers were published as a Journal of Machine Learning Research Workshop and Conference Proceedings, Volume 4.
     
  • Y. Saeys, I. Inza, P. Larrañaga (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507-2517.
     
  • J.L. Flores, I. Inza, P. Larrañaga (2007). Wrapper discretization by means of estimation of distribution algorithms. Intelligent Data Analysis Journal, 11(5), 525-545.
  • A. Pérez, P. Larrañaga, I. Inza (2006). Supervised classification with conditional Gaussian networks: increasing the structure complexity from naive Bayes. International Journal of Approximate Reasoning, 43(1), 1-25.
  • P. Larrañaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armañanzas, G. Santafé, A. Pérez, V. Robles (2006). Machine Learning in Bioinformatics. Briefings in Bioinformatics, 7(1), 86-112.
  • P. Larrañaga, J.A. Lozano, J.M. Peña, I. Inza (guest editors, 2005). Special issue on "Probabilistic Graphical Models for Classification". Machine Learning, vol. 59(3).
  • R. Blanco, I. Inza, M. Merino, J. Quiroga and P. Larrañaga (2005). Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. Journal of Biomedical Informatics, vol. 38(5), 376-388.
  • I. Inza, P. Larrañaga, R. Blanco, A.J. Cerrolaza (2004). Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence in Medicine, special issue in "Data mining in genomics and proteomics", 31(2), 91-103.
  • R. Blanco, P. Larrañaga, I. Inza, B. Sierra (2004). Gene selection for cancer classification using wrapper approaches. International Journal of Pattern Recognition and Artificial Intelligence, 18(8), 1373-1390.
  • R. Blanco, I. Inza, P. Larrañaga (2003). Learning Bayesian networks in the space of structures by estimation of distribution algorithms. International Journal of Intelligent Systems, 18, 205-220.
  • I. Inza, B. Sierra, R. Blanco, P. Larrañaga (2002). Gene selection by sequential wrapper approaches in microarray cancer class prediction. Journal of Intelligent and Fuzzy Systems, 12/1, 25-34.
  • I. Inza, M. Merino, P. Larrañaga, J. Quiroga, B. Sierra, M. Girala (2001). Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS. Artificial Intelligence in Medicine, 23/2, 187-205.
  • I. Inza, P. Larrañaga, B. Sierra (2001). Feature Subset Selection by Bayesian networks: a comparison with genetic and sequential algorithms. International Journal of Approximate Reasoning, 27/2, 143-164.
  • B.Sierra, N. Serrano, P.Larrañaga, E.J. Plasencia, I. Inza, J.J. Jimenez, P. Revuelta, M.L. Mora (2001). Using Bayesian networks in the construction of a bi-level multi-classifier. Artificial Intelligence in Medicine, 22, 233-248.
  • J.M. Peña, J.A. Lozano, P. Larrañaga, I. Inza (2001). Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 590-603.
     
  • I. Inza, P. Larrañaga, R. Etxeberria, B. Sierra (2000). Feature Subset Selection by Bayesian networks based optimization. Artificial Intelligence, 123(1-2), 157-184.
     
  • I. Inza, P. Larrañaga, B. Sierra, R. Etxeberria, J.A. Lozano, J.M. Peña (1999). Representing the joint behaviour of machine learning inducers by Bayesian networks. Pattern Recognition Letters, 20 (11-13), 1201-1209.
  • P. Larrañaga, C. Kuijpers, R. Murga, I. Inza, S. Dizdarevich (1999). Evolutionary algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review, 13, 129-170.
    
    
• New book on Estimation of Distribution Algorithms (EDAs). "Towards a New Evolutionary Computation" (Springer, 2006). Lozano, Larrañaga, Inza, Bengoetxea (eds.)
• My Doctoral Dissertation, "Advances in Supervised Classification based on Probabilistic Graphical Models" (June, 2002). (Supervisor: Pedro Larrañaga)
  
   
• Book Chapters:
  
  
  • I. Inza, B. Calvo, R. Armañanzas, E. Bengoetxea, P. Larrañaga, J.A. Lozano (2010). Machine learning: an indispensable tool in bioinformatics. R. Matthiesen (ed.). Bioinformatics Methods in Clinical Research. Springer. (2nd chapter of the book).
  • P. Larrañaga, I. Inza, J.L. Flores (2005). A guide to the literature on inferring genetic networks by probabilistic graphical models. F. Azuaje, J. Dopazo (eds.). Data Analysis and Visualization in Genomics and Proteomics. John Wiley and Sons Ltd., 215-238.
  • I. Inza, R. Armañanzas, G. Santafé (2006). Una aproximación al software WEKA. Aprendizaje Automático: conceptos básicos y avanzados. B. Sierra (ed.), chapter 23, pp. 477-483. Pearson Educación, Madrid. (In Spanish).
  • R. Blanco, I. Inza, P. Larrañaga (2004). Learning Bayesian networks by floating search methods. Advances in Bayesian Networks. J.A. Gámez, S. Moral, A. Salmerón (eds.), Physica Verlag - Springer Verlag, 181-200.
  • I. Inza, P. Larrañaga, B. Sierra (2001). Estimation of Distribution Algorithms for feature subset selection in large dimensionality domains. Data Mining: A Heuristic Approach. H. Abbass, R. Sarker, C. Newton (eds.), IDEA Group Publishing, 97-116.
  • I. Inza, P. Larrañaga, B. Sierra (2001). Feature Subset Selection by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms. A new tool for Evolutionary Computation. P. Larrañaga, J.A. Lozano (eds.), Kluwer Academic Publishers.
  • I. Inza, P. Larrañaga, B. Sierra (2001). Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms. A new tool for Evolutionary Computation. P. Larrañaga, J.A. Lozano (eds.), Kluwer Academic Publishers.
  • B. Sierra, E. Jiménez, I. Inza, P. Larrañaga, J. Muruzábal (2001). Rule Induction by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms. A new tool for Evolutionary Computation. P. Larrañaga, J.A. Lozano (eds.), Kluwer Academic Publishers.
    
    
    
• Refereed Conference Papers:
  
  
  • M. Martinez-Garcia, S. Garcia-Gutierrez, R. Armañanzas, A. Diaz, I. Inza, J.A. Lozano (2022). Learning a battery of COVID-19 mortality prediction models by multi-objective optimization. International Conference on Artificial Intelligence in Medicine (AIME'2022). Halifax, Canada.
    
  • A. Arregi, I. Inza, I. Bediaga (2022). Vibration analysis for rotatory elements wear detection in paper mill machine. International Conference on Database and Expert Systems Applications (DEXA). Vienna, Austria.
    
  • M. Soric, D. Pongrac, I. Inza (2020). Using convolutional neural network for chest X-ray image classification. 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). Opatija, Croatia.
    
  • P. Rozas-Larraondo, I. Inza, J.A. Lozano (2017). Automating weather forecasts based on convolutional networks. ICML 2017 Workshop on Deep Structured Prediction. Sydney, Australia.
    
  • J. Hernández, I. Inza, J.A. Lozano (2015). A novel weakly supervised problem: learning from positive-unlabeled proportions. Spanish Conference on Artificial Intelligence. Albacete, Spain.
  • J. Hernández, I. Inza, J.A. Lozano (2013). Learning from crowds in multi-dimensional classification domains. Spanish Conference on Artificial Intelligence. Lecture Notes in Computer Science 8109, "Advances in Artificial Intelligence", 352-362. Madrid, Spain.
  • A. Pérez, I. Inza, J.A. Lozano (2013). Multidimensional k-interaction classifier: taking advantage of all the information contained in low order interactions. Spanish Conference on Artificial Intelligence. Lecture Notes in Computer Science 8109, "Advances in Artificial Intelligence", 393-401. Madrid, Spain.
  • J.A. Fernandes, J.A. Lozano, I. Inza, X. Irigoien, J.D. Rodríguez, A. Pérez (2013). Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting. ICES (International Council for the Exploration of the Sea) Annual Science Conference. Session on "Advantages of Bayesian analysis for fisheries and ecological research". Reykjavik, Iceland.
  • J. Hernández, I. Inza (2011). Learning naive Bayes models for multiple-instance learning with label proportions. Spanish Conference on Artificial Intelligence. Lecture Notes in Artificial Intelligence 7023, 134-144. San Cristobal de La Laguna, Tenerife, Spain.
  • J.A. Fernandes, X. Irigoien, N. Goikoetxea, A. Uriarte, J.A. Lozano, I. Inza (2009). Robust approaches to supervised machine learning techniques for seven fish species recruitment prediction in fisheries. ICES/PICES/UNCOVER Symposium 2009 on Rebuilding Depleted Fish Stocks - Biology, Ecology, Social Science and Management Strategies. Warnemünde/Rostock, Germany.
  • J.A. Fernandes, X. Irigoien, A. Uriarte, L. Ibaibarriaga, J.A. Lozano, I. Inza (2009). Anchovy recruitment mixed long series prediction using supervised classification. Working document to the ICES benchmark workshop on short lived species (WKSHORT). Bergen, Norway.
  • R. Armañanzas, B. Calvo, I. Inza, P. Larrañaga, I. Bernales, A. Fullaondo, A.M. Zubiaga (2007). Bayesian classifiers with consensus gene selection: a case study in the systemic lupus erythematosus. 14th European Conference for Mathematics in Industry, Progress in Industrial Mathematics ECMI'06. Madrid, Spain, 560-565.
  • A. García, A. Freije, R. Armañanzas, I. Inza, Z. Ispizua, P. Heredia, P. Larrañaga, G. López Vivanco, T. Suárez, M. Betanzos (2007). Gene expression model for the classification of human colorectal cancer and potential CRC biomarkers search. Drug Discovery Technology, poster session. London, UK.
  • A. Pérez, P. Larrañaga, I. Inza (2006). Information theory and classification error in probabilistic classifiers. Discovery Science, DS-2006, Lecture Notes in Computer Science 4265. Barcelona, Spain, 347-351.
  • A. García, A. Freije, R. Armañanzas, I. Inza, Z. Ispizua, P. Heredia, P. Larrañaga, G. López Vivanco, T. Suárez, M. Betanzos (2006). Simultaneous search of genomic and proteomic biomarkers in human colorectal cancer. Genomes to Systems Conference, poster session. Manchester, UK.
     

Teaching

I teach in 3th course of the Computer Engineering Degree the "Data Mining" subject.
I also used to teach "Lineal optimization" (2nd course, teached in basque language=euskara).
I have (co)supervised the following Computer Engineering Degree Thesis projects in the Computer Engineering Faculty.

I teach in the following Master-Program a "Advanced Machine Learning" subject: "Master on Computational Engineering and Intelligent Systems" (taught in the Computer Science Faculty, University of the Basque Country). I have (co)supervised the following Master Thesis projects in this Master program.

I also teach a "Machine Learning" course in the "Erasmus Mundus - Language and Communication Technology" Master.