Classification of wine quality with imbalanced data

Hu, Gongzhu; Tan, Xi; Mohammed, Faraz; Miao, Huaikou

doi:10.1109/icit.2016.7475021

Cited by 31 publications

(15 citation statements)

References 15 publications

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“…Additionally, volatile acidity is scored higher in the stacked generalization and OCIRF plots, unlike in the comparison work. The decreased emphasis of sulphates and increased emphasis of volatile acidity is consistent with other rankings on this dataset such as those presented by Hu et al [15] and Cortez [6]. This again exemplifies the potential advantages for combining multiple variable importance scores to gain a comprehensive assessment as to the gravity of each predictor variable, rather than making judgments based upon just one.…”

Section: F I G U R Esupporting

confidence: 88%

An analytical toast to wine: Using stacked generalization to predict wine preference

Larkin

McManus

2020

Statistical Analysis

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Due to the intricacies surrounding taste profiles, one's view of good wine is subjective. Therefore, it is advantageous to provide a more objective, data‐driven way to assess wine preferences. Motivated by a previous study that modeled wine preferences using machine learning algorithms, this work presents an ensemble approach to predict a wine sample's quality level given its physiochemical properties. Results show the proposed framework out‐performs many sophisticated models including the one recommended by the motivational study. Moreover, the proposed framework offers a simple variable importance strategy to gain insight as to the relevance of the predictor variables and is applied to both simulated and real data. Given the predictive power of using ensembles, especially when they can be interpretable, practitioners can use the following approach to provide an accurate and inferential perspective towards demystifying wine preferences.

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Section: F I G U R Esupporting

confidence: 88%

An analytical toast to wine: Using stacked generalization to predict wine preference

Larkin

McManus

2020

Statistical Analysis

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“…Berikut ini studi yang telah dilakukan terkait penanganan terhadap ketidakseimbangan kelas, pada beberapa studi tesebut menggunakan beberapa pendekatan sebagai solusinya. Seperti penelitian yang dilakukan oleh [12], [13] dan [14]. Mereka membuktikan bahwa penerapan teknik resampling atau pendekatan level data untuk menangani ketidakseimbangan kelas pada dataset dapat meningkatkan kinerja dari algoritme klasifikasi.…”

Section: Pendahuluanunclassified

Penerapan Metode Ensemble Untuk Meningkatkan Kinerja Algoritme Klasifikasi Pada Imbalanced Dataset

Pristyanto

2019

JTI

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Pada bidang data mining sering kali para peneliti tidak memperhatikan keseimbangan distribusi kelas pada dataset. Hal ini dapat menimbulkan kesulitan yang cukup serius pada algoritme klasifikasi. karena secara teori mayoritas classifier mengasumsikan distribusi yang relatif seimbang, sehingga menyebabkan kinerja suatu algoritme klasifikasi menjadi kurang maksimal. Oleh karena itu, pada penelitian ini diterapkan metode ensemble dengan penambahan adaptive boosting untuk menyelesaikan permasalahan tersebut. Dari hasil pengujian yang dilakukan pada penelitian ini, metode ensemble dengan penambahan adaptive boosting dapat meningkatkan nilai kinerja algoritme klasifikasi. Nilai kinerja algoritme Naive Bayes dengan Adaptive Boosting akurasi yang dihasilkan sebesar 91.98%, sensitifitas sebesar 91.98%, spesifisitas sebesar 96.49%, dan g-mean sebesar 94.21%. Nilai kinerja algoritme Support Vector Machine dengan Adaptive Boosting akurasi yang dihasilkan sebesar 91.52%, sensitifitas sebesar 91.52%, spesifisitas sebesar 96.29%, dan g-mean sebesar 93.88%. Sedangkan Nilai kinerja algoritme Decision Tree dengan Adaptive Boosting akurasi yang dihasilkan sebesar 94.37%, sensitifitas sebesar 94.37%, spesifisitas sebesar 97.73%, dan g-mean sebesar 96.03%. Hal ini menunjukkan bahwa metode ensemble dengan Adaptive Boosting dapat menjadi solusi untuk meningkatkan kinerja algoritme pada imbalanced dataset.Kata Kunci: adaptive boosting, data mining, ensemble, ketidakseimbangan kelas, klasifikasi.

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“…(5) The log value to handle division with the denominator is more than the numerator value. Weighting is carried out normalization so that it becomes equation (6).…”

Section: Spam Detectionmentioning

confidence: 99%

“…Data imbalance or imbalanced dataset is a significant difference between the number of minority class instances and majority class instances [5]. The imbalanced dataset can pose a risk of misclassification of the dataset so that the performance of a classification algorithm is not optimal [6] because it assumes that the class distribution in the dataset is relatively balanced and the cost of classification errors is the same. When the percentage of a minority class sample is less than 20%, the dataset is considered to be very unbalanced and the classifier's performance continues to deteriorate when the majority sample percentage decreases [7].…”

Section: Introductionmentioning

confidence: 99%

Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes

Haqimi¹,

Rokhman²,

Priyanta³

2019

Indonesian J. Comput. Cybern. Syst.

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Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.

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Classification of wine quality with imbalanced data

Cited by 31 publications

References 15 publications

An analytical toast to wine: Using stacked generalization to predict wine preference

An analytical toast to wine: Using stacked generalization to predict wine preference

Penerapan Metode Ensemble Untuk Meningkatkan Kinerja Algoritme Klasifikasi Pada Imbalanced Dataset

Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes

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