• A two-stage algorithm in evolutionary product unit neural networks for classification 

      Tallón-Ballesteros, Antonio J.; Hervás-Martínez, César (Elsevier, 2011)
      This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of ...
    • An Extended Approach of a Two-Stage Evolutionary Algorithm in Artificial Neural Networks for Multiclassification Tasks 

      Tallón-Ballesteros, Antonio J.; Hervás-Martínez, César; Gutiérrez, Pedro A. (Springer, 2016)
      This chapter considers a recent algorithm to add broader diversity at the beginning of the evolutionary process and extends it to sigmoidal neural networks. A simultaneous evolution of architectures and weights is performed ...
    • A Classification Module for Genetic Programming Algorithms in JCLEC 

      Cano, Alberto; Luna, J.M.; Zafra, Amelia; Ventura Soto, S. (MIT Press, 2014)
      JCLEC-Classi cation is a usable and extensible open source library for genetic program- ming classi cation algorithms. It houses implementations of rule-based methods for clas- si cation based on genetic programming, ...
    • Democratization of advanced models for data science 

      Barbudo Lunar, Rafael (Universidad de Córdoba, UCOPress, 2024)
      En las últimas décadas, la mayoría de las empresas y organizaciones han generado y almacenado enormes cantidades de datos procedentes de diversas fuentes como transacciones financieras, interacciones con clientes, registros ...
    • Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity 

      Barbudo Lunar, Rafael; Ramírez, Aurora; Romero, José Raúl (Elsevier, 2024)
      The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this ...
    • Jewelry Recognition via Encoder-Decoder Models 

      Alcalde-Llergo, José M.; Yeguas-Bolívar, Enrique; Zingoni, Andrea; Fuerte-Jurado, Alejandro (IEEE, 2023)
      Jewelry recognition is a complex task due to the different styles and designs of accessories. Precise descriptions of the various accessories is something that today can only be achieved by experts in the field of ...
    • MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning 

      Belmonte Pérez, Álvaro Andrés; Zafra, Amelia; Gibaja, Eva (Elsevier, 2022)
      MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities ...
    • Minería de datos educativos para la detección de recursos clave 

      Gibaja, Eva; Zafra Gómez, Amelia; Luque Rodríguez, María; Arauzo Azofra, Antonio; Ramírez Quesada, Aurora; Olmo Ortiz, Juan Luis (UCOPress, 2017)
      Este artículo describe un proyecto de innovación educativa centrado en diseñar y desarrollar un nuevo módulo de Moodle que permita obtener modelos predictivos basados en árboles de decisión a partir de los datos de uso ...
    • Multi-Objective Genetic Programming for Feature Extraction and Data Visualization 

      Cano, Alberto; Ventura Soto, S.; Cios, Krzyztof J. (2017)
      Feature extraction transforms high dimensional data into a new subspace of lower dimensionalitywhile keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. ...
    • Parallel evaluation of Pittsburgh rule-based classifiers on GPUs 

      Cano, Alberto; Zafra, Amelia; Ventura Soto, S. (2017-01-19)
      Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level ...
    • Speeding up Multiple Instance Learning Classification Rules on GPUs 

      Cano, Alberto; Zafra, Amelia; Ventura Soto, S. (2017)
      Multiple instance learning is a challenging task in supervised learning and data mining. How- ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing ...
    • ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data 

      Cano, Alberto; Nguyen, Dat T.; Ventura Soto, S.; Cios, Krzysztof J. (2015-10-15)
      Supervised discretization is one of basic data preprocessing techniques used in data mining. CAIM (Class- Attribute InterdependenceMaximization) is a discretization algorithm of data for which the classes are known. ...