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.
|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|
|Number of pages||10|
|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
|Name||Advances in Intelligent Systems and Computing|
|Conference||1st FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020|
|Period||17/01/20 → 19/01/20|
Bibliographical noteFunding 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.
© 2021, Springer Nature Singapore Pte Ltd.
- Ant colony optimization
- Machine learning
- Reduction dimensionality