A Design and analysis of classification models for the gelification of alkoxybenzoates using the kNN algorithm

Authors

  • Virginia Loredo-Pong Instituto Tecnológico de Ciudad Madero
  • María Lucila Morales-Rodríguez Centro de Investigación en Petroquímica, División de Estudios de Posgrado e Investigación. Tecnológico Nacional de México-Instituto Tecnológico de Ciudad Madero.
  • Nancy Patricia Díaz-Zavala Centro de Investigación en Petroquímica, División de Estudios de Posgrado e Investigación. Tecnológico Nacional de México-Instituto Tecnológico de Ciudad Madero.
  • Nelson Rangel-Valdez Centro de Investigación en Petroquímica, División de Estudios de Posgrado e Investigación. Tecnológico Nacional de México-Instituto Tecnológico de Ciudad Madero.
  • Jaime E. Sosa-Sevilla Centro de Investigación en Petroquímica, División de Estudios de Posgrado e Investigación. Tecnológico Nacional de México-Instituto Tecnológico de Ciudad Madero.

Keywords:

machine learning, predictive models, HSP

Abstract

The classification models of the states produced by the gelation tests of alkoxy benzoates require designing several corpora of data based on their characteristics. This work studies a series of alkoxybenzoates and 15 solvents characterized by Hansen Solubility Parameters and the number of carbons on the alkyl tail as a distinctive structural feature for the molecules. These properties were evaluated as attributes on the corpora on the kNN algorithm. Different configurations developed were analyzed, with three corpora designed varying their content according to their attributes. From this study, seem the relevance of some attributes over others on the performance prediction of the products class obtained. The significant samples correctly classified on corpora containing HSP and the number of carbons on the alkyl ether tail of alkoxybenzoates denote the influence of these properties on the classification. Also, the more suitable configurations on kNN, metric, k value, attribute weight is founded according to each corpus.

Published

2022-08-15

How to Cite

Loredo-Pong, V., Morales-Rodríguez, M. . L. . ., Díaz-Zavala, N. . P., Rangel-Valdez, N., & Sosa-Sevilla, J. E. . (2022). A Design and analysis of classification models for the gelification of alkoxybenzoates using the kNN algorithm. International Journal of Combinatorial Optimization Problems and Informatics, 13(2), 58–64. Retrieved from https://ijcopi.org/ojs/article/view/266

Issue

Section

SI Business Analytics

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