Now showing items 1-4 of 4

    • 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, ...
    • 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 Evolutionary Learning Algorithms using GPUs 

      Cano, Alberto; Zafra, Amelia; Ventura Soto, S. (ESTYLF, 2010)
      This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA CUDA GPUs to reduce the computational time due to the poor perfor- mance in large problems. Two di erent clas- si ...
    • Speeding up Multiple Instance Learning Classification Rules on GPUs 

      Cano, Alberto; Zafra, Amelia; Ventura Soto, S. (2017-01-19)
      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 ...