Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents

Rosario Nery Huanca-Gonza, Julio Vera-Sancho, Edward Hinojosa-Cárdenas, Carlos Eduardo Arbieto-Batallanos, María Del Carmen Córdova-Martinez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Dimensionality reduction is crucial in Machine Learning, to obtain main characteristics. The method of selecting characteristics that we will use is a multivariate filter, where we will jointly evaluate the relevance between the characteristics; using unsupervised learning. For which we will use information from Institute of Education Sciences, and application of TF-IDF to obtain the weights of each word in each document. To perform the dimensionality reduction, the PUFSACO (Parallelization Unsupervised future selection based on Ant Colony Optimization) algorithm will be applied, due to the large amount of information that will be processed. The output of PUFSACO will be the input of the classification algorithm. The present work proposes to parallelize the UFSACO algorithm (Unsupervised future selection based on Ant Colony Optimization). Being the basis of PUFSACO, comparing the computational time to validate the improvement of the proposed algorithm, the results show that applying parallelization improves 117% than the original algorithm.

Idioma originalInglés
Título de la publicación alojadaRising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020
EditoresVijay Singh Rathore, Nilanjan Dey, Vincenzo Piuri, Rosalina Babo, Zdzislaw Polkowski, João Manuel R.S. Tavares
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas463-472
Número de páginas10
ISBN (versión impresa)9789811560132
DOI
EstadoPublicada - 2021
Evento1st FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020 - Jaipur, India
Duración: 17 ene. 202019 ene. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1187
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia1st FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020
País/TerritorioIndia
CiudadJaipur
Período17/01/2019/01/20

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.

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