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Classification Rule Mining with Iterated Greedy 

Pedraza, Juan A.; García-Martínez, Carlos; Cano, Alberto; Ventura Soto, S. (2017-03-30)
In the context of data mining, classi cation rule discovering is the task of designing accurate rule based systems that model the useful knowledge that di erentiate some data classes from others, and is present in large ...
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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, ...
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Multi-Objective Genetic Programming for Feature Extraction and Data Visualization 

Cano, Alberto; Ventura Soto, S.; Cios, Krzyztof J. (2017-03-31)
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. ...
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JCLEC Meets WEKA! 

Cano, Alberto; Luna, J.M.; Olmo Ortiz, Juan Luis; Ventura Soto, S. (2014-02-27)
WEKA has recently become a very referenced DM tool. In spite of all the functionality it provides, it does not include any framework for the development of evolutionary algorithms. An evolutionary computation framework ...
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Parallelization Strategies for Markerless Human Motion Capture 

Cano, Alberto; Yeguas-Bolivar, Enrique; Medina-Carnicer, R.; Ventura Soto, S.; Muñoz-Salinas, Rafael (2015-10-15)
Markerless Motion Capture (MMOCAP) is the problem of determining the pose of a person from images captured by one or several cameras simultaneously without using markers on the subject. Evaluation of the solutions is ...
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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 ...
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Cifrado de imágenes y Matemáticas 

Rojas, Ángela; Cano, Alberto (Universidad Nacional de La Plata, 2011)
Un tema que debe interesar al profesorado de Matemáticas de todos los niveles educativos es cómo hacer comprender a nuestros alumnos la utilidad de los conceptos matemáticos que están estudiando en nuestras asignaturas. ...
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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. ...
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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 ...
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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 ...
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