Ángel Díaz-Pacheco

Dr. Ángel Díaz-Pacheco

Department of Electronic Engineering, Universidad de Guanajuato, Salamanca, Guanajuato, Mexico
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Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 January 2024 in Information Technology & Tourism
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Journal Article
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Ángel Díaz-PachecoÁngel Díaz-Pacheco
Ángel Díaz-Pacheco
RGRafael Guerrero-Rodríguez
Rafael Guerrero-Rodríguez
Miguel Ángel Álvarez CarmonaMiguel Ángel Álvarez Carmona
Miguel Ángel Álvarez Carmona
Ansel Yoan Rodríguez GonzálezAnsel Yoan Rodríguez González
Ansel Yoan Rodríguez González
Ramon ArandaRamon Aranda
Ramon Aranda

1 December 2023 in Information Technology & Tourism
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Journal Article
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RGRafael Guerrero-Rodríguez
Rafael Guerrero-Rodríguez
MÁMiguel Á. Álvarez-Carmona
Miguel Á. Álvarez-Carmona
RARamón Aranda
Ramón Aranda
Ángel Díaz-PachecoÁngel Díaz-Pacheco
Ángel Díaz-Pacheco

1 October 2023 in Journal of King Saud University Computer and Information Sciences
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Journal Article
Open access
Ángel Díaz-PachecoÁngel Díaz-Pacheco
Ángel Díaz-Pacheco
RGRafael Guerrero-Rodríguez
Rafael Guerrero-Rodríguez
Miguel Ángel Álvarez CarmonaMiguel Ángel Álvarez Carmona
Miguel Ángel Álvarez Carmona
Ansel Yoan Rodríguez GonzálezAnsel Yoan Rodríguez González
Ansel Yoan Rodríguez González
Ramon ArandaRamon Aranda
Ramon Aranda

5 October 2021 in Sādhanā
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Journal Article
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Ángel Díaz-PachecoÁngel Díaz-Pacheco
Ángel Díaz-Pacheco
CRCarlos A Reyes-García
Carlos A Reyes-García
VCVanesa Chicatto-Gasperín
Vanesa Chicatto-Gasperín

26 March 2024 in ACM Transactions on Knowledge Discovery From Data
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Journal Article
Open access
Ansel Yoan Rodríguez GonzálezAnsel Yoan Rodríguez González
Ansel Yoan Rodríguez González
Ramon ArandaRamon Aranda
Ramon Aranda
Miguel Ángel Álvarez CarmonaMiguel Ángel Álvarez Carmona
Miguel Ángel Álvarez Carmona
Ángel Díaz-PachecoÁngel Díaz-Pacheco
Ángel Díaz-Pacheco
Rosa María Valdovinos-RosasRosa María Valdovinos-Rosas
Rosa María Valdovinos-Rosas
Frequent similar pattern mining (FSP mining) allows for finding frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, necessitating the development of more efficient algorithms for FSP mining. This work aims to improve the efficiency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A data structure to condense an object description collection, named FV-Tree , and an algorithm for mining all FSPs from the FV-Tree , named X-FSPMiner , are proposed. The experimental results reveal that the novel algorithm X-FSPMiner vastly outperforms the state-of-the-art algorithms for mining all FSPs using Boolean and non-increasing monotonic similarity functions.

18 June 2021 in Journal of Experimental & Theoretical Artificial Intelligence
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Journal Article
Closed access
Ángel Díaz-PachecoÁngel Díaz-Pacheco
Ángel Díaz-Pacheco
CRCarlos Alberto Reyes-Garcia
Carlos Alberto Reyes-Garcia
Improvement of accuracy in classifiers is a crucial topic in the machine learning field. The problem has been addressed, making new algorithms and selecting the fittest classifier for a given dataset. The latter approach combined with feature selection and pre-processing form up a new paradigm known as Full Model Selection. This paradigm is like a black box whose input is a dataset, and as an output, a precise classification model is obtained. Despite that, full model selection is not the first alternative with the larger datasets of nowadays. We propose the use of MapReduce to deal with huge datasets, a bio-inspired optimisation algorithm and the use of a novel algorithm based on fuzzy classification rules as a proxy model to guide the optimisation process. To the best of our knowledge, this work is the first to propose a classification algorithm based on fuzzy rules as a proxy model. Obtained results showed an accuracy improvement and a considerable reduction of the computing time in datasets of a wide range of sizes.