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A Bayesian Approach for Raisin Data Classification

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dc.contributor.author Kumari, H.M.N.S.
dc.contributor.author Nawarathne, U.M.M.P.K.
dc.date.accessioned 2024-01-16T04:37:23Z
dc.date.available 2024-01-16T04:37:23Z
dc.date.issued 2023
dc.identifier.citation Kumari H.M.N.S.; Nawarathne U.M.M.P.K. (2023), A Bayesian Approach for Raisin Data Classification, International Research Conference on Smart Computing and Systems Engineering (SCSE 2023), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. Page 9 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/27347
dc.description.abstract Raisin performs a decisive role in the commodity economy. Recently, low-quality raisin products have been introduced to agricultural markets worldwide. Therefore, it is crucial to identify a suitable classification method to distinguish between varieties of raisins. Previous research has employed various traditional machine learning methods to classify commodities. However, it is challenging to quantify uncertainties through traditional machine learning models. Therefore, this study employed a Bayesian Logistic Regression (BLR) model using seven morphological features of two varieties of raisins grown in Turkey. Initially, different machine learning techniques were employed on data. After that, four priors, such as Jefferys, Laplace, Cauchy, and Gaussian, were considered, and hyperparameters were tuned using the empirical Bayes method. Marginal posterior distributions of the model parameters were estimated, and the convergence of the models was checked. Then, evaluation metrics of the BLR model with different priors were compared to those of machine learning models. According to the results, the BLR model with Gaussian prior produced the highest accuracy of 93%. Finally, it can be concluded that the BLR model with Gaussian prior provides substantially better results when classifying raisin data. en_US
dc.publisher Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka en_US
dc.subject Bayesian classification, Bayesian logistic regression, classification, machine learning, raisin en_US
dc.title A Bayesian Approach for Raisin Data Classification en_US


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