A Design and analysis of classification models for the gelification of alkoxybenzoates using the kNN algorithm
Keywords:machine learning, predictive models, HSP
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.