Quadratic vector support machine algorithm, applied to prediction of university student satisfaction

Abstract

This study aims to identify the most optimal supervised learning algorithm to be applied to the prediction of satisfaction of university students. In this study, the IBM SPSS - 25.0 software was used to test the reliability of the satisfaction questionnaire and the MATLAB R2021b software through the classification learner technique to determine the supervised learning algorithm. The experimental results determine a Cronbach's Alpha reliability of 0.979, in terms of the classification algorithm, it is validate d that the quadratic vector support machine (SVM) has better performance metrics, being correct in 97.8% (a ccuracy) in the predictions of satisfaction of university students, with a r ecall (sensitivity) of 96.5% and an F1 score of 0.968. Likewise, when eva luating the classification model by means of the receiver operating characteristic curve (ROC) technique, it is identified that for the three expected classes of satisfaction the value of the area under the curve (AUC) is equal to 1, in such sense the pred ictive model through the SVM Quadratic algorithm, has a high capacity to distinguish between the 3 classes ; i) d issatisfied, ii) s atisfied and iii) v ery satisfied of satisfaction of university students.

Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin

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Determination of support vector regression parameters using African buffalo optimization algorithm

“…For any regression function f(p), there is a loss function L that determine the amount of deviation by the function's output as from the actual value. In this paper we adopted the commonly use loss function that was proposed by Gunn [29] and formulated as (2).…”

“…Hence, grid search and CV methods are considered not suitable choice for determining SVR parameters. This led researchers to explore swarm intelligence (SI) methods to optimize optimal parameters of SVR algorithm as can be established in literature [2], [3], [5], [17]- [21]. SI algorithms like PSO has been widely used for parameter optimization for several algorithms including SVR.…”

Estimation of biomass of forage sorghum (sorghum bicolor) Cv. Samurai-2 using support vector regression

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