Publicacions en què col·labora amb JUAN JOSE RODRIGUEZ DIEZ (43)

2024

  1. Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques

    Information Fusion, Vol. 102

  2. Disturbing neighbors en un contexto semisupervisado

    XX Conferencia de la Asociación Española para la Inteligencia Artificial

  3. Ensemble methods and semi-supervised learning for information fusion: A review and future research directions

    Information Fusion, Vol. 107

  4. SSProteinFitnessPrediction

    Zenodo

  5. SSProteinFitnessPrediction

    Zenodo

2022

  1. 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

2018

  1. Local sets for multi-label instance selection

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

  2. 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

  3. 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)

  2. Diversity techniques improve the performance of the best imbalance learning ensembles

    Information Sciences, Vol. 325, pp. 98-117

  3. Random Balance: Ensembles of variable priors classifiers for imbalanced data

    Knowledge-Based Systems, Vol. 85, pp. 96-111

2014

  1. Tree ensemble construction using a GRASP-based heuristic and annealed randomness

    Information Fusion, Vol. 20, Núm. 1, pp. 189-202