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

doi:10.1007/978-981-15-6014-9_53

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Rising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020
Editors	Vijay Singh Rathore, Nilanjan Dey, Vincenzo Piuri, Rosalina Babo, Zdzislaw Polkowski, João Manuel R.S. Tavares
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	463-472
Number of pages	10
ISBN (Print)	9789811560132
DOIs	https://doi.org/10.1007/978-981-15-6014-9_53
State	Published - 2021
Event	1st FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020 - Jaipur, India Duration: 17 Jan 2020 → 19 Jan 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1187
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020
Country/Territory	India
City	Jaipur
Period	17/01/20 → 19/01/20

Bibliographical note

Funding Information:
The research work was developed thanks to the research project IBA0029-2016. ?Servicios de Vigilancia Tecnol?gica para centros de investigaci?n y Aula de Innovaci?n Tecnol?gica, Orientadas al Desarrollo de Proyectos I+D+I en TICs y Educaci?n? We thank the ?Universidad Nacional de San Agust?n de Arequipa? for making possible the realization of the research article.

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

Keywords

Ant colony optimization
Machine learning
Reduction dimensionality

Access to Document

10.1007/978-981-15-6014-9_53

Cite this

Huanca-Gonza, R. N., Vera-Sancho, J., Hinojosa-Cárdenas, E., Arbieto-Batallanos, C. E., & Córdova-Martinez, M. D. C. (2021). Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents. In V. S. Rathore, N. Dey, V. Piuri, R. Babo, Z. Polkowski, & J. M. R. S. Tavares (Eds.), Rising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020 (pp. 463-472). (Advances in Intelligent Systems and Computing; Vol. 1187). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-6014-9_53

Huanca-Gonza, Rosario Nery ; Vera-Sancho, Julio ; Hinojosa-Cárdenas, Edward et al. / Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents. Rising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020. editor / Vijay Singh Rathore ; Nilanjan Dey ; Vincenzo Piuri ; Rosalina Babo ; Zdzislaw Polkowski ; João Manuel R.S. Tavares. Springer Science and Business Media Deutschland GmbH, 2021. pp. 463-472 (Advances in Intelligent Systems and Computing).

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abstract = "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.",

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Huanca-Gonza, RN, Vera-Sancho, J, Hinojosa-Cárdenas, E, Arbieto-Batallanos, CE & Córdova-Martinez, MDC 2021, Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents. in VS Rathore, N Dey, V Piuri, R Babo, Z Polkowski & JMRS Tavares (eds), Rising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020. Advances in Intelligent Systems and Computing, vol. 1187, Springer Science and Business Media Deutschland GmbH, pp. 463-472, 1st FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020, Jaipur, India, 17/01/20. https://doi.org/10.1007/978-981-15-6014-9_53

Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents. / Huanca-Gonza, Rosario Nery; Vera-Sancho, Julio; Hinojosa-Cárdenas, Edward et al.
Rising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020. ed. / Vijay Singh Rathore; Nilanjan Dey; Vincenzo Piuri; Rosalina Babo; Zdzislaw Polkowski; João Manuel R.S. Tavares. Springer Science and Business Media Deutschland GmbH, 2021. p. 463-472 (Advances in Intelligent Systems and Computing; Vol. 1187).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Vera-Sancho, Julio

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Huanca-Gonza RN, Vera-Sancho J, Hinojosa-Cárdenas E, Arbieto-Batallanos CE, Córdova-Martinez MDC. Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents. In Rathore VS, Dey N, Piuri V, Babo R, Polkowski Z, Tavares JMRS, editors, Rising Threats in Expert Applications and Solutions - Proceedings of FICR-TEAS 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 463-472. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-15-6014-9_53

Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents

Abstract

Publication series

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Bibliographical note

Keywords

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