Guillermo Sánchez-Díaz

Orcid: 0000-0002-5480-8983

According to our database1, Guillermo Sánchez-Díaz authored at least 30 papers between 2001 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Shortest-length and coarsest-granularity constructs vs. reducts: An experimental evaluation.
Int. J. Approx. Reason., 2024

2022
A review of algorithms to computing irreducible testors applied to feature selection.
Artif. Intell. Rev., 2022

2020
An Algorithm for Computing Minimum-Length Irreducible Testors.
IEEE Access, 2020

2018
Enhancing the Performance of YYC Algorithm Useful to Generate Irreducible Testors.
Int. J. Pattern Recognit. Artif. Intell., 2018

2017
A CUDA-based hill-climbing algorithm to find irreducible testors from a training matrix.
Pattern Recognit. Lett., 2017

2016
A new algorithm for computing reducts based on the binary discernibility matrix.
Intell. Data Anal., 2016

2015
On the relation between rough set reducts and typical testors.
Inf. Sci., 2015

A parallel hill-climbing algorithm to generate a subset of irreducible testors.
Appl. Intell., 2015

Computing Constructs by Using Typical Testor Algorithms.
Proceedings of the Pattern Recognition - 7th Mexican Conference, 2015

2014
An evolutionary algorithm with acceleration operator to generate a subset of typical testors.
Pattern Recognit. Lett., 2014

Are Reducts and Typical Testors the Same?
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2014

2013
An Algorithm for Computing Typical Testors Based on Elimination of Gaps and Reduction of columns.
Int. J. Pattern Recognit. Artif. Intell., 2013

Clasificadores basados en arboles de decisión para grandes conjuntos de datos.
Computación y Sistemas, 2013

Easy Categorization of Attributes in Decision Tables Based on Basic Binary Discernibility Matrix.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2013

2012
A Fast Implementation for the Typical Testor Property Identification Based on an Accumulative Binary Tuple.
Int. J. Comput. Intell. Syst., 2012

Building fast decision trees from large training sets.
Intell. Data Anal., 2012

Parallel k-Most Similar Neighbor Classifier for Mixed Data.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2012, 2012

2011
Decision tree induction using a fast splitting attribute selection for large datasets.
Expert Syst. Appl., 2011

Typical Testors Generation Based on an Evolutionary Algorithm.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2011, 2011

2010
A Fast Implementation of the CT_EXT Algorithm for the Testor Property Identification.
Proceedings of the Advances in Soft Computing, 2010

Radon Transform Algorithm for Fingerprint Core Point Detection.
Proceedings of the Advances in Pattern Recognition, 2010

Multivariate Decision Trees Using Different Splitting Attribute Subsets for Large Datasets.
Proceedings of the Advances in Artificial Intelligence, 2010

2008
A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2008

2007
CT-EXT: An Algorithm for Computing Typical Testor Set.
Proceedings of the Progress in Pattern Recognition, 2007

FS-EX Plus: A New Algorithm for the Calculation of Typical FS-Testor Set.
Proceedings of the Progress in Pattern Recognition, 2007

2005
An Incremental Clustering Algorithm Based on Compact Sets with Radius alpha.
Proceedings of the Progress in Pattern Recognition, 2005

2004
An Image Resizing Algorithm for Binary Maps.
Proceedings of the 5th Mexican International Conference on Computer Science (ENC 2004), 2004

2003
Determination of Similarity Threshold in Clustering Problems for Large Data Sets.
Proceedings of the Progress in Pattern Recognition, 2003

2001
LC: A Conceptual Clustering Algorithm.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2001

A Clustering Method for Very Large Mixed Data Sets.
Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November, 2001


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