Prediction of Quality for Different Type of Wine based on Different Feature Sets Using Supervised Machine Learning Techniques
| Autor: | Kueh Lee Hui, Satyabrata Aich, Mangal Sain, Ahmed Abdulhakim Al-Absi |
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| Rok vydání: | 2019 |
| Předmět: |
Wine
business.industry Computer science media_common.quotation_subject Feature extraction Probabilistic logic 020206 networking & telecommunications Feature selection 02 engineering and technology Machine learning computer.software_genre Support vector machine Feature (computer vision) Genetic algorithm Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business computer media_common |
| Zdroj: | 2019 21st International Conference on Advanced Communication Technology (ICACT). |
| Popis: | In recent years, most of the industries promoting their products based on the quality certification they received on the products. The traditional way of assessing the product quality is time consuming, however with the invent of machine learning techniques the processes has become more efficient and consumed less time than before. In this paper we have explored, some of the machine learning techniques to assess the quality of wine based on the attributes of wine that depends on quality. We have used white wine and red wine quality dataset for this research work. We have used different feature selection technique such as genetic algorithm (GA) based feature selection and simulated annealing (SA) based feature selection to check the prediction performance. We have used different performance measure such as accuracy, sensitivity, specificity, positive predictive value, negative predictive value for comparison using different feature sets and different supervised machine learning techniques. We have used nonlinear, linear and probabilistic classifiers. We have found that feature selection-based feature sets able to provide better prediction than considering all the features for performance prediction.We have found accuracy ranging from 95.23% to 98.81% with different feature sets. This analysis will help the industries to access the quality of the products at less time and more efficient way. |
| Databáze: | OpenAIRE |
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