A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning

Elreedy, Dina; Atiya, Amir F.; Kamalov, Firuz

doi:10.1007/s10994-022-06296-4

Cited by 70 publications

(19 citation statements)

References 60 publications

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“…Information gain involves entropy to find the best term. If the information gain value of a term is greater, then the feature is considered more significant and more important [28].…”

Section: Feature Selectionmentioning

confidence: 99%

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

Fatah,

Rochman,

Setiawan

et al. 2024

E3S Web Conf.

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Sentiment analysis is natural language processing (NLP) that uses text analysis to recognize and extract opinions in text. Analysis is used to convert unstructured information into more structured information, also to determine whether an object has a positive, negative, or neutral tendency, and is an effort to facilitate decision making for tourism managers as a recommendation in developing tourist attractions. In this study, opinions were conducted on tourism reviews in Bangkalan using the Naïve Bayes method. This method is a machine learning algorithm to classify text into concepts that are easy to understand and provide accurate results with high efficiency. This method is proven to provide excellent results with a high level of accuracy, especially for large data, but has some drawbacks, sensitive to feature selection. Thus, a feature selection process is needed to improve classification efficiency by reducing the amount of data analyzed, with the Information Gain feature selection method. The word weighting method uses TF-IDF, while the data used comes from google maps reviews taken through web scraping, where tourist visitors provide reviews and ratings of places that have been visited. However, the large number of reviews can make it difficult for tourist attractions managers to manage them, so the process of labeling the sentiment class of the review data obtained 3649 reviews, with 2583 positive, 275 negative, and 457 neutral. Based on the test results that have been carried out using the Information Gain threshold of 0.0001, 0.0003, and 0.0007 can improve the accuracy of the Naïve Bayes model, for the best test at threshold 0.0007, with an accuracy value of 78.68%, precision 80.44%, recall 82.59%, and f1-score 82.53%, from the test results it shows that the use of information gain feature selection and SMOTE technique has a fairly good performance in classifying public opinion sentiment data on tourism in Bangkalan Regency, meaning that tourism management is good seen from the results of visitor satisfaction sentiment.

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“…Information gain involves entropy to find the best term. If the information gain value of a term is greater, then the feature is considered more significant and more important [28].…”

Section: Feature Selectionmentioning

confidence: 99%

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

Fatah,

Rochman,

Setiawan

et al. 2024

E3S Web Conf.

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“…Oversampling by creating new instances can produce unsatisfactory results if the newly formed samples are wrongly thought to be part of the minority class based only on their closeness to existing minority examples. When dealing with imbalanced datasets, it is important to avoid conflating the usage of simulated data with oversampling class-imbalanced data [53,54].…”

Section: Datamentioning

confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

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“…In this paper we have utilized total of two different ML's to explore, explain and model the behavior of brucellosis disease with respect to several input parameters 12 . The ML's are selected bases on their suitability and acceptability are given below:…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Application of Machine Learning for Predicting Brucellosis Disease in Dairy Cattle

Hussaini,

Tito,

Hasan

et al. 2023

Preprint

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Brucellosis, a zoonotic disease caused by Brucella bacteria, has emerged as a significant concern in Bangladesh, affecting both animals and humans, with economic repercussions and health risks. This paper addresses the urgent need for effective disease management in a country where agriculture and livestock are pivotal to the economy. The study focuses on identifying risk factors for brucellosis in dairy cattle and their correlation with factors collected from Central Cattle Breeding and Dairy Farm (CCBDF) and Military Dairy Farms (MDF) in Bangladesh. Serum and milk samples, along with demographic data, were collected and analyzed. To address the challenge of imbalanced data, the study introduces the application of the "SMOTE" Weka filter for the first time in Bangladesh's veterinary profession. This balances the dataset, enhancing the accuracy of predictive modeling. Machine learning models, specifically Multilayer Perceptron (MLP) and J48 decision tree algorithms, are employed for brucellosis prediction. MLP achieved a Correct Classification (CC) rate of 95.0801%, while J48 achieved 94.0503%, demonstrating their efficacy. Sensitivity analysis identifies key attributes, with retained placenta being the most significant, aiding in prioritizing control measures. Association Rules reveal patterns and relationships among disease-related factors, enhancing understanding and proactive prevention.

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A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning

Cited by 70 publications

References 60 publications

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Application of Machine Learning for Predicting Brucellosis Disease in Dairy Cattle

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