Assessing wine quality using a decision tree

Lee, Seunghan; Park, Ju‐Young; Kang, Kyungtae

doi:10.1109/syseng.2015.7302752

Cited by 19 publications

(8 citation statements)

References 7 publications

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“…The comparative analysis led to the conclusion that classification can help in improving the quality of wine during production. The proposed decision-tree based method [18] proved to be better than other machine learning models such as LibSVM (Support vector machine), Bayes Net, MultIPerceptron (Multi-layer perceptron). The various performance metrics used were Precision, Recall, F-measure, and Accuracy and found that the proposed method had the best performance with the values of 60.10, 60.70, 60.30 ,60.66 respectively.…”

Section: Related Workmentioning

confidence: 96%

Detailed Study of Wine Dataset and its Optimization

Dhaliwal¹,

Sharma²,

Chauhan³

2022

IJISA

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The consumption of wine these days is becoming more common in social gatherings and to monitor the health of individuals it's very important to maintain the quality of the wine. For the assessment of wine quality many methods have been proposed. We have described a technique to pre-process the “Vinho Verde” wine dataset. The dataset consists of red and white wine samples. The wine dataset size has been reduced from a total of 13 attributes to 9 attributes without any loss of performance. This has been validated through various classification techniques like Random Forest Classifier, Decision tree Classifiers, K-Nearest Neighbor Classifier and Artificial Neural Network Classifier. These classifiers have been compared based on two performance metrics of accuracy and RMSE values. Among the three classifiers Random Forest tends to outperform the other two classifiers in various measures for predicting the quality of the wine.

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Section: Related Workmentioning

confidence: 96%

Detailed Study of Wine Dataset and its Optimization

Dhaliwal¹,

Sharma²,

Chauhan³

2022

IJISA

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“…The proportion of accurately predicted positive observations to all expected positive observations is known as precision. [20], [22]. It is given in (2).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…If all the attributes in the dataset are numerical, there is no need for any encoding. This work also checks for duplicates on the dataset and takes action accordingly [22]. Figure 1 gives the various steps involved in data preprocessing.…”

Section: Data Preprocessingmentioning

confidence: 99%

An investigation of wine quality testing using machine learning techniques

Mani

Swaminathan

et al. 2023

IJ-AI

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Quality is the most determining factor for any product. Optimal care and best measures are to be taken in assessing the quality of any product. This work deals with determining the quality of wine using intelligence-based learning techniques. In order to estimate the quality of wine, several experiments are performed on wine datasets. The main purpose of our work is to study and discover an efficient machine learning (ML) model that could determine the quality of wine given some Physico-chemical features. This study establishes that selecting important features to evaluate rather than all of them can lead to improved forecasts. According to the results, this approach may provide people who are not wine experts a greater opportunity to choose a fine wine.</span></p></div></div></div>

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“…It is given in (2). Recall is the proportion of accurately anticipated positive observations to all of the actual class observations that we have observed [20], [22]. The equation for the calculation of recall is given in (3).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

An Investigation of Wine Quality Testing using Machine Learning Techniques

2020

jxu

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Quality is the most determining factor for any product. Optimal care and best measures are to be taken in assessing the quality of any product. This work deals with determining the quality of wine using intelligence-based learning techniques. In order to estimate the quality of wine, several experiments are performed on wine datasets. The main purpose of our work is to study and discover an efficient machine learning (ML) model that could determine the quality of wine given some Physico-chemical features. This study establishes that selecting important features to evaluate rather than all of them can lead to improved forecasts. According to the results, this approach may provide people who are not wine experts a greater opportunity to choose a fine wine.

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Assessing wine quality using a decision tree

Cited by 19 publications

References 7 publications

Detailed Study of Wine Dataset and its Optimization

Detailed Study of Wine Dataset and its Optimization

An investigation of wine quality testing using machine learning techniques

An Investigation of Wine Quality Testing using Machine Learning Techniques

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