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 Gladys Castillo. I co-supervised the European-PhD thesis of Rubén Armañanzas, Jose A. Fernándes, Aritz Pérez and Rosa Blanco. I currently co-supervise the PhD projects of Jerónimo Hernández and Jonathan Ortigosa. post: Iñaki Inza Computer Science Faculty (University of the Basque Country) Paseo Manuel de Lardizabal 1  (Campus Ibaeta) 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 and Optimization Search Heuristics in general                - Learning of Bayesian networks in general, specially for classification
   - Feature Subset Selection in Machine Learning and Data Mining (FSS)      - Search Heuristics: Estimation of Distribution Algorithms (EDAs)
   - Non-classical classification scenarios: weakly supervised classification, semi-supervised classification, learning from group probabilities...

   - Applications in Biomedicine and Bioinformatics: DNA microarrays, microRNAs and mass spectrometry data in diagnosis-prognosis-classification tasks -- biomarker identification
   - Applications in Oceanographic and Ecological data
   - Applications in Sentiment Analysis
  
   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-Journals:
  
  • J. Hernández, I. Inza, J.A. Lozano (2013). Learning Bayesian network classifiers from label proportions. Pattern Recognition. Accepted for publication.
     
  • 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.)
• You can download my Doctoral Dissertation, "Advances in Supervised Classification based on Probabilistic Graphical Models" (June, 2002).
  
   
• 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 in recent years:
  
  
  • 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 supervised the following Computer Engineering Degree Thesis projects in the Computer Engineering Faculty.

I teach in the following Master-Program a "Data Mining" subject: "Master on Computational Engineering and Intelligent Systems" (taught in the Computer Science Faculty, University of the Basque Country).