An in-depth performance analysis of the oversampling techniques for high-class imbalanced dataset

Wibowo, Prasetyo Adi; Fatichah, Chastine

doi:10.26594/register.v7i1.2206

Cited by 13 publications

(5 citation statements)

References 24 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dataset contains 100,000 movie ratings by MovieLens users and consists of three files: a rating file, a movie information file, and a user file. Systematic sampling is a random sampling method that involves selecting sample units at certain intervals from the desired target population [15]. This method is carried out by determining the interval between units taken randomly from the population, and then choosing the first unit at random.…”

Section: Methodsmentioning

confidence: 99%

Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System

Sri Lestari,

M. Elrico Afdila,

Yan Aditiya Pratama

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

A recommendation system is a system that provides suggestions or recommendations for a product or service for its users. One of the problems encountered in the recommendation system is sparsity, namely the lack of available data for analysis, resulting in poor performance of the recommendation system because it cannot provide the proper recommendations. On this basis, this study proposes the mean method and the stochastic Hot-Deck Method to calculate missing values to improve the quality of the recommendations. The experimental results show that the hot-deck imputation method gives better results than the mean imputation method with smaller RMSE and MAE values, namely 2,706 and 2,691.

show abstract

Section: Methodsmentioning

confidence: 99%

Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System

Sri Lestari,

M. Elrico Afdila,

Yan Aditiya Pratama

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

show abstract

“…An unbalanced data set, where the number of instances in each category is significantly different, can lead to poor classification results. The class imbalance occurs when the distribution of classes between the majority and minority classes is different [33]. In the case of our data set consisting of seven unbalanced categories, removing categories with little data (truncated, impossible to check, and other) and replacing partially true categories with true and partially false categories with false ones is a valid approach to balancing the data set.…”

Section: Algorithm 1 the Process Of Web Scraper Designmentioning

confidence: 99%

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

Bucos

Tucudean

2023

Applied Sciences

View full text Add to dashboard Cite

This paper aims to investigate the use of a Romanian data source, different classifiers, and text data augmentation techniques to implement a fake news detection system. The paper focusses on text data augmentation techniques to improve the efficiency of fake news detection tasks. This study provides two approaches for fake news detection based on content and context features found in the Factual.ro data set. For this purpose, we implemented two data augmentation techniques, Back Translation (BT) and Easy Data Augmentation (EDA), to improve the performance of the models. The results indicate that the implementation of the BT and EDA techniques successfully improved the performance of the classifiers used in our study. The results of our content-based approach show that an Extra Trees Classifier model is the most effective, whether data augmentation is used or not, as it produced the highest accuracy, precision, F1 score, and Kappa. The Random Forest Classifier with BT yielded the best results of the context-based experiment overall, with the highest accuracy, recall, F1 score, and Kappa. Furthermore, we found that BT and EDA led to an increase in the AUC scores of all models in both content-based and context-based data sets.

show abstract

“…This method synthesizes data adaptively based on the distribution of positive samples [15]. The advantage of ADASYN is that it can focus data duplication on only one specific area [16], where samples are produced more in areas with low minority sample densities than in areas with high densities. This increase in distribution can reduce data imbalances and help improve classification [17].…”

Section: Adaptive Synthetic Sampling (Adasyn)mentioning

confidence: 99%

The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance

Kaope,

Pristyanto

2023

matrik

View full text Add to dashboard Cite

Class imbalance is a condition where the amount of data in the minority class is smaller than that of the majority class. The impact of the class imbalance in the dataset is the occurrence of minority class misclassification, so it can affect classification performance. Various approaches have been taken to deal with the problem of class imbalances such as the data level approach, algorithmic level approach, and cost-sensitive learning. At the data level, one of the methods used is to apply the sampling method. In this study, the ADASYN, SMOTE, and SMOTE-ENN sampling methods were used to deal with the problem of class imbalance combined with the AdaBoost, K-Nearest Neighbor, and Random Forest classification algorithms. The purpose of this study was to determine the effect of handling class imbalances on the dataset on classification performance. The tests were carried out on five datasets and based on the results of the classification the integration of the ADASYN and Random Forest methods gave better results compared to other model schemes. The criteria used to evaluate include accuracy, precision, true positive rate, true negative rate, and g-mean score. The results of the classification of the integration of the ADASYN and Random Forest methods gave 5% to 10% better than other models.

show abstract

An in-depth performance analysis of the oversampling techniques for high-class imbalanced dataset

Cited by 13 publications

References 24 publications

Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System

Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance

Contact Info

Product

Resources

About