Listar Producción Científica por autor "6938b01c-fc3b-49a4-80c8-6d4d95d77aaf"
Mostrando ítems 1-7 de 7
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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, ... -
Data mining in predictive maintenance systems: A taxonomy and systematic review
Esteban, Aurora; Zafra, Amelia; Ventura, Sebastián (Wiley, 2022)Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored ... -
Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
Esteban, Aurora; Zafra, Amelia; Romero Morales, C. (Elsevier, 2020)The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help ... -
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 ... -
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)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 ...