Publications (26)

2018

  1. An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects

    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 8, Núm. 1

  2. Una primera aproximación para la extracción de patrones emergentes en flujos continuos de datos

    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. Pareto based ensemble with feature and instance selection for learning from multi-class imbalanced datasets

    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. Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de grandes vol´umenes de datos y flujos continuos de datos dir.

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

  5. MOEA-EFEP: un algoritmo evolutivo multi-objetivo para la extracción de patrones emergentes difusos

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

  6. Atipicidad: medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas

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

  7. MOEA-EFEP: Multi-Objective Evolutionary Algorithm for Extracting Fuzzy Emerging Patterns

    IEEE Transactions on Fuzzy Systems, Vol. 26, Núm. 5, pp. 2861-2872

  8. Improvement of subgroup descriptions in noisy data by detecting exceptions

    Progress in Artificial Intelligence, Vol. 7, Núm. 1, pp. 55-64

  9. A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy

    Knowledge-Based Systems, Vol. 139, pp. 89-100

2017

  1. Analysing concentrating photovoltaics technology through the use of emerging pattern mining

    Advances in Intelligent Systems and Computing

  2. Analysing Concentrating Photovoltaics Technology Through the Use of Emerging Pattern Mining

    International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings

  3. A first approach to handle fuzzy emerging patterns mining on big data problems: The EvAEFP-spark algorithm

    IEEE International Conference on Fuzzy Systems

  4. MEFASD-BD: Multi-objective evolutionary fuzzy algorithm for subgroup discovery in big data environments - A MapReduce solution

    Knowledge-Based Systems, Vol. 117, pp. 70-78

  5. A pareto-based ensemble with feature and instance selection for learning from multi-class imbalanced datasets

    International Journal of Neural Systems, Vol. 27, Núm. 6

2016

  1. Estimating the maximum power delivered by concentrating photovoltaics technology through atmospheric conditions using a differential evolution approach

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

  2. The influence of noise on the evolutionary fuzzy systems for subgroup discovery

    Soft Computing, Vol. 20, Núm. 11, pp. 4313-4330

  3. Subgroup discovery with evolutionary fuzzy systems in r: The SDEFSR package

    R Journal, Vol. 8, Núm. 2, pp. 307-323

  4. A View on Fuzzy Systems for Big Data: Progress and Opportunities

    International Journal of Computational Intelligence Systems, Vol. 9, pp. 69-80

2015

  1. A first approach in the class noise filtering approaches for fuzzy subgroup discovery

    Advances in Intelligent Systems and Computing

  2. A differential evolution proposal for estimating the maximum power delivered by CPV modules under real outdoor conditions

    Expert Systems with Applications, Vol. 42, Núm. 13, pp. 5452-5462