Journal papers

Pérez-Núñez, P., Díez, J., Remeseiro, B., Luaces, O., and Bahamonde. A. (2023) All-in-one picture: visual summary of items in a recommender system. Neural Computing and Applications, vol. 35, pp. 20339–20349, 2023. (DOI: 10.1007/s00521-023-08822-4)

Pérez-Núñez, P., Díez, J., Luaces, O., Remeseiro, B., and Bahamonde, A. (2023). Users’ photos of items can reveal their tastes in a recommender system. Information Sciences, vol. 642, pp. 119227. (DOI: 10.1016/j.ins.2023.119227)

G. de la Cruz, M. Lira, O. Luaces and B. Remeseiro (2022). Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection. IEEE Transactions on Neural Networks and Learning Systems, in press. (DOI: 10.1109/TNNLS.2022.3202643)

Pérez-Núñez, P., Díez, J., Luaces, O., and Bahamonde, A. (2021). User encoding for clustering in very sparse recommender systems tasks. Multimedia Tools and Applications. (DOI: 10.1007/s11042-021-11564-x)

J. Díez, P. Pérez-Núñez, O. Luaces, B. Remeseiro, A. Bahamonde. (2020) Towards Explainable Personalized Recommendations by Learning from Users’ Photos. Information Sciences, vol. 520, pp. 416-430. (DOI: 10.1016/j.ins.2020.02.018)

P. Pérez-Núñez, O. Luaces, A. Bahamonde, J. Díez. (2019) Improving recommender systems by encoding items and user profiles considering the order in their consumption history. Prog. Artif. Intell., vol. 9, pp. 67-75. (DOI: 10.1007/s13748-019-00199-7)

J. Díez, D. Martínez-Rego, A. Alonso-Betanzos, O. Luaces, and A. Bahamonde, (2019) Optimizing novelty and diversity in recommendations, Prog. Artif. Intell. vol. 8 pp. 101-119. (doi: 10.1007/s13748-018-0158-4) 

O. Luaces, J. Díez, and A. Bahamonde. (2018) A peer assessment method to provide feedback, consistent grading and reduce students’ burden in massive teaching settings, Comput. Educ., vol. 126, no. March, pp. 283–295. (DOI: 10.1016/j.compedu.2018.07.016)

S. Pang, J. J. del Coz, Z. Yu, O. Luaces, and J. Díez, (2017) Deep Learning and Preference Learning for Object Tracking: A Combined Approach, Neural Process. Lett., pp. 70–77. (DOI: 10.1007/s11063-017-9720-5)

O. Luaces, J. Díez, A. Alonso-Betanzos, A. Troncoso, and A. Bahamonde, (2017) Content-based methods in peer assessment of open-response questions to grade students as authors and as graders, Knowledge-Based Syst., vol. 117, pp. 79–87. (DOI: 10.1016/j.knosys.2016.06.024)

S. Pang, J. J. del Coz, Z. Yu, O. Luaces, and J. Díez, (2017) Deep learning to frame objects for visual target tracking, Eng. Appl. Artif. Intell., vol. 65, no. May, pp. 406–420. (DOI: 10.1016/j.engappai.2017.08.010)

J. Díez, J. del Coz, O. Luaces, and A. Bahamonde (2016). Using tensor products to detect unconditional label dependence in multilabel classifications. Information Sciences, vol. 329, pp. 20-32. (DOI: 10.1016/j.ins.2015.08.055)

O. Luaces, J. Díez, A. Alonso-Betanzos, A. Troncoso, and A. Bahamonde (2015). A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments. Knowledge-Based Systems, 85:322-328. (DOI: 10.1016/j.knosys.2015.05.019)

O. Luaces, J. Díez, T. Joachims, and A. Bahamonde (2015). Mapping preferences into euclidean space. Expert Systems with Applications, 42(22):8588 – 8596. (DOI: 10.1016/j.eswa.2015.07.013)

J. Díez, O. Luaces, J. J. del Coz, and A. Bahamonde (2015). “Optimizing different loss functions in multilabel classifications”. Progress in Artificial Intelligence, 3(2):107-118. (DOI: 10.1007/s13748-014-0060-7)

E. de Andrés Galiana, J. Fernández-Martínez, O. Luaces, J. del Coz, R. Fernández, J. Solano, E. Nogués, Y. Zanabilli, J. Alonso, A. Payer, J. Vicente, J. Medina, F. Taboada, M. Vargas, C. Alarcón, M. Morán, A. González-Ordóñez, M. Palicio, S. Ortiz, C. Chamorro, S. Gonzalez, and A. González-Rodríguez (2015). On the prediction of hodgkin lymphoma treatment response. Clinical and Translational Oncology, 17(8):612 – 619. (DOI: 10.1007/s12094-015-1285-z)

G. Lastra, O. Luaces, and A. Bahamonde (2014). “Interval prediction for graded multi-label classification”. Pattern Recognition Letters, 49(0):171 – 176. (DOI: 10.1016/j.patrec.2014.07.005)

J.A. Sánchez del Rivero, E. Montañés-Roces, B. de la Roza-Delgado, A. Soldado, O. Luaces, J,.R. Quevedo, and A. Bahamonde (2013). “Feature selection for classification of animal feed ingredients from near infrared microscopy spectra”. Information Sciences, 241:58–69. (DOI: 10.1016/j.ins.2013.03.054)

J.R. Quevedo, A. Bahamonde, M. Pérez-Enciso, and O. Luaces (2012). “Disease liability prediction from large scale genotyping data using classifiers with a reject option”. IEEE Transactions on Computational Biology and Bioinformatics, 9(1):88–97. (doi:10.1109/TCBB.2011.44)

J. R. Quevedo, O. Luaces, and A. Bahamonde (2012). “Multilabel Classifiers with a Probabilistic Thresholding”. Pattern Recognition, 45:876–883 (DOI: 10.1016/j.patcog.2011.08.007).

O. Luaces, L.H.A. Rodrigues, C.A. Alves Meira, and A. Bahamonde (2011). “Using nondeterministic learners to alert on coffee rust disease”. Expert Systems with Applications, 38(11):14276–14283. (doi:10.1016/j.eswa.2011.05.003)

O. Luaces, J.R. Quevedo, M. Perez-Enciso, J. Díez, J.J. del Coz, and A. Bahamonde (2010). “Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models”, Journal of Computational Biology, 17(12):1711–1723. (doi:10.1089/cmb.2009.0161)

O. Luaces, F. Taboada, G. M. Albaiceta, L. A. Domínguez, P. Enríquez, and A. Bahamonde (2009). “Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples”. Artificial Intelligence in Medicine, 45(1):63 – 76.

J. Díez, J. J. del Coz, O. Luaces, and A. Bahamonde (2008), “Clustering people according to their preference criteria,” Expert Systems with Applications, 34(2):1274–1284.

J. R. Quevedo, A. Bahamonde, and O. Luaces, “A simple and efficient method for variable ranking according to their usefulness for learning,” Computational Statistics & Data Analysis, vol. 52, pp. 578–595, 2007.

A. Bahamonde, J. Díez, J. R. Quevedo, O. Luaces, and J. J. del Coz (2007). “How to learn consumer preferences from the analysis of sensory data by means of support vector machines (SVM),” Trends in Food Science & Technology, vol. 18, pp. 20–28.

J. Díez, A. Bahamonde, J. Alonso, S. López, J. del Coz, J. Quevedo, J. Ranilla, O. Luaces, I. Álvarez, L. Royo, and F. Goyache, “Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses,” Meat Science, vol. 64, no. 3, pp. 249–258, 2003.

O. Luaces and A. Bahamonde, “Inflating examples to obtain rules,” International Journal of Intelligent Systems, vol. 18, pp. 1113–1143, November 2003.

J. Ranilla, O. Luaces, and A. Bahamonde, “A heuristic for learning decision trees and pruning them into classification rules,” AI Communications, vol. 16, no. 2, pp. 71–87, 2003.

F. Goyache, A. Bahamonde, J. Alonso, S. López, J. J. del Coz, J. Quevedo, J. Ranilla, O. Luaces, I. Álvarez, L. Royo, and J. Díez, “The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry,” Trends in Food Science & Technology, vol. 12, no. 10, pp. 370–381, 2001.

F. Goyache, J. del Coz, J. Quevedo, S. López, J. Alonso, J. Ranilla, O. Luaces, I. Álvarez, and A. Bahamonde, “Using artificial intelligence to design and implement a morphological assessment system in beef cattle,” Animal Science, vol. 73, pp. 49–60, 2001.

J. Díez, J. Ranilla, and O. Luaces, “Aplicación de un proceso de selección de reglas a un sistema de aprendizaje ajeno al nivel de impureza,” Inteligencia ArtificialRevista Iberoamericana de IA, no. 15, pp. 10–18, 2001.

O. Luaces and A. Bahamonde, “Aprendizaje de la similitud entre casos con valores discretos y numéricos,” Revista Iberoamericana de Inteligencia Artificial, vol. 9, pp. 38–44, 2000.

S. López, F. Goyache, J. Quevedo, J. Alonso, J. Ranilla, O. Luaces, A. Bahamonde, and J. del Coz, “Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles,” Revista Iberoamericana de Inteligencia Artificial, vol. 4, no. 10, pp. 5–17, 2000.

Books / Chapters

J. del Coz and O. Luaces, Aprendizaje Automático: conceptos básicos y avanzados, ch. Métodos Kernel y Máquinas de Vectores Soporte, pp. 163–200. Pearson, 2006.

O. Luaces, Un Sistema de Aprendizaje de Reglas Explícitas mediante la Generalización de Instancias. PhD thesis, Departamento de Informática, Universidad de Oviedo en Gijón, Sep. 1999.