2014

(2014) Prat, G., Belanche, Ll. Improved Stability of Feature Selection by Combining Instance and Feature Weighting. AI-2014: The Thirty-fourth SGAI International Conference Cambridge, UK. Research and Development in Intelligent Systems XXXI. Bramer, Max, Petridis, Miltos (Eds.), Springer-Verlag. ISBN 978-3-319-12069-0

(2014) L. Rentería, F.F. González, M. Stilianova, Ll. Belanche, B.L. Flores, J.E. Ibarra. Glucose Oxidase Biosensor Modeling by Machine Learning Methods. Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Tuxtla Gutiérrez, Mexico

(2014) Sánchez F., Soler, A., López, D. Martín, C., Ageno, A., Belanche Ll., Cabré, J., Cobo, E., Farré, R., García, J., Marés, P. Developing professional skills at tertiary level: A model to integrate competencies across the curriculum. Annual Frontiers in Education Conference pp. 1090-1098

(2014) Ll. Belanche, J. Hernández. Similarity networks for classification: a case study in the Horse Colic problem. Technical Report LSI-14-4-R.

(2014) A. Vilamala, Ll. Belanche. Improving Stability of Feature Selection for Brain Tumour Diagnosis Using $^1$H-MRS Data. IWBBIO 2014 (2nd International Work-Conference on Bioinformatics and Biomedical Engineering), Granada, Spain.

(2014) A. Vilamala, Ll. Belanche, A. Vellido. A MAP approach for Convex Non-negative Matrix Factorization in the Diagnosis of Brain Tumors. International Workshop on Pattern Recognition in Neuroimaging (PRNI 2014). Tübingen, Germany.

(2014) F.F. González, Ll. Belanche. Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy. Computación y sistemas Vol. 18 (2), pp. 275-293.

(2014) Ll. Belanche, V. Kobayashi, T. Aluja. Handling missing values in kernel methods with application to microbiology data. Neurocomputing, Vol. 141, p. 110-116.

(2014) L. Rentería, Ll. Belanche, F.F. González, M. Stilianova. Modeling a Second-Generation Glucose Oxidase Biosensor with Statistical Machine Learning Methods. In M. Stoytcheva & J.F. Osma (Eds.) Biosensors: Recent advances and mathematical challenges, pp. 163-183. OmniaScience. ISBN: 978-84-941872-0-9.