Alvar Arnaiz Gonzalez-rekin lankidetzan egindako argitalpenak (18)

2022

  1. Correction to: Rotation Forest for multi-target regression (International Journal of Machine Learning and Cybernetics, (2022), 13, 2, (523-548), 10.1007/s13042-021-01329-1)

    International Journal of Machine Learning and Cybernetics

  2. Rotation Forest for multi-target regression

    International Journal of Machine Learning and Cybernetics, Vol. 13, Núm. 2, pp. 523-548

  3. When is resampling beneficial for feature selection with imbalanced wide data?

    Expert Systems with Applications, Vol. 188

2021

  1. Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark

    Neurocomputing, Vol. 464, pp. 432-437

  2. Experimental evaluation of ensemble classifiers for imbalance in Big Data

    Applied Soft Computing, Vol. 108

  3. Rotation forest for big data

    Information fusion, Vol. 74, pp. 39-49

2020

  1. An experimental evaluation of mixup regression forests

    Expert Systems with Applications, Vol. 151

  2. Random Balance ensembles for multiclass imbalance learning

    Knowledge-Based Systems, Vol. 193

2018

  1. La automatización de aprendizaje automático

    Un enfoque multidisciplinar de la optimización (Servicio de Publicaciones e Imagen Institucional), pp. 119-145

  2. Local sets for multi-label instance selection

    Applied Soft Computing Journal, Vol. 68, pp. 651-666

  3. Local sets for multi-label instance selection

    XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España

  4. Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning

    Expert Systems with Applications, Vol. 109, pp. 114-130

2016

  1. Instance selection for regression by discretization

    Expert Systems with Applications, Vol. 54, pp. 340-350

  2. Instance selection for regression: Adapting DROP

    Neurocomputing, Vol. 201, pp. 66-81

  3. Instance selection of linear complexity for big data

    Knowledge-Based Systems, Vol. 107, pp. 83-95

  4. Random feature weights for regression trees

    Progress in Artificial Intelligence, Vol. 5, Núm. 2, pp. 91-103

2015

  1. An experimental study on combining binarization techniques and ensemble methods of decision trees

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2012

  1. Tool for supporting the teaching of instance selection algorithms

    EDULEARN12: 4th International Conference on Education and New Learning Technologies