CRISTOBAL JOSE
CARMONA DEL JESUS
Ikertzaileren 2014-2017 tartean
![Foto de María José del](/img/nophoto.png)
María José del
Jesús Díaz
María José del Jesús Díaz-rekin lankidetzan egindako argitalpenak (23)
2018
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A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy
Knowledge-Based Systems, Vol. 139, pp. 89-100
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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
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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
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Improvement of subgroup descriptions in noisy data by detecting exceptions
Progress in Artificial Intelligence, Vol. 7, Núm. 1, pp. 55-64
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MOEA-EFEP: Multi-Objective Evolutionary Algorithm for Extracting Fuzzy Emerging Patterns
IEEE Transactions on Fuzzy Systems, Vol. 26, Núm. 5, pp. 2861-2872
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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
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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
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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
2017
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A first approach to handle fuzzy emerging patterns mining on big data problems: The EvAEFP-spark algorithm
IEEE International Conference on Fuzzy Systems
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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
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Analysing concentrating photovoltaics technology through the use of emerging pattern mining
Advances in Intelligent Systems and Computing
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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
2016
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A View on Fuzzy Systems for Big Data: Progress and Opportunities
International Journal of Computational Intelligence Systems, Vol. 9, pp. 69-80
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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)
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Subgroup discovery with evolutionary fuzzy systems in r: The SDEFSR package
R Journal, Vol. 8, Núm. 2, pp. 307-323
2015
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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
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A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans
Information Sciences, Vol. 298, pp. 180-197
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FuGePSD: Fuzzy Genetic Programming-based algorithm for Subgroup Discovery
Proceedings of the 2015 conference of the international fuzzy systems association and the european society for fuzzy logic and technology
2014
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Applying subgroup discovery based on evolutionary fuzzy systems for web usage mining in e-commerce: A case study on OrOlivesur.com
Advances in Intelligent Systems and Computing
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Overview on evolutionary subgroup discovery: Analysis of the suitability and potential of the search performed by evolutionary algorithms
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 4, Núm. 2, pp. 87-103