Publicaciones en las que colabora con DIEGO JESÚS GARCÍA GIL (19)

2025

  1. Developing Big Data anomaly dynamic and static detection algorithms: AnomalyDSD spark package

    Information Sciences, Vol. 690

2024

  1. Analysis of a Parallel and Distributed BPSO Algorithm for EEG Classification: Impact on Energy, Time and Accuracy

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

  2. Benchmarking Anomaly Detection Methods: Insights From the UCR Time Series Anomaly Archive

    Expert Systems

  3. Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

    Cognitive Computation, Vol. 16, Núm. 4, pp. 1572-1588

2023

  1. Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series

    Information Fusion, Vol. 100

  2. Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study

    Neurocomputing, Vol. 544

2021

  1. Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms

    Neurocomputing, Vol. 462, pp. 440-452

2020

  1. Big Data Preprocessing: Enabling Smart Data

    Springer International Publishing, pp. 1-186

  2. Big data ensembles for classification and smart data extraction

    Big data ensembles for classification and smart data extraction

2019

  1. A first approach on big data missing values imputation

    IoTBDS 2019 - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security

  2. Big data preprocessing as the bridge between big data and smart data: Bigdapspark and Bigdapflink libraries

    IoTBDS 2019 - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security

  3. Enabling Smart Data: Noise filtering in Big Data classification

    Information Sciences, Vol. 479, pp. 135-152

  4. From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

    International Journal of Intelligent Systems, Vol. 34, Núm. 12, pp. 3260-3274

  5. Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data

    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 9, Núm. 2

2018

  1. Bagging-RandomMiner: un algoritmo en mapreduce para detección de anomalías en big data

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

  2. MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

    Knowledge-Based Systems, Vol. 153, pp. 176-192

  3. On the use of random discretization and dimensionality reduction in ensembles for big data

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

  4. Principal Components Analysis Random Discretization Ensemble for Big Data

    Knowledge-Based Systems, Vol. 150, pp. 166-174

  5. Smart Data: Filtrado de Ruido para Big Data

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