Oversampling with Reliably Expanding Minority Class Regions for Imbalanced Data Learning

Zhu, Tuanfei; Liu, Xinwang; Zhu, En

doi:10.1109/tkde.2022.3171706

Cited by 8 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 2 is a techniques to tackle class imbalance learning that can be classified into three main categories: (a) Data-level methods, (b) Algorithm-level methods, and (c) combination of both methods [11], [17].…”

Section: Class Imbalanced Learningmentioning

confidence: 99%

“…Several previous studies have implemented undersampling [14], [17], [22], [30] to reduce the size of large sample data to balance different types of sample data. Beside undersampling, previous studies have also implemented an oversampling [11], [12], [13], [27] method that takes small samples as the object to generate new samples. Imbalanced data in text classification with multi-class need to be considered since a classification model that is usually based on a fair class distribution could have problems with imbalanced class [6].…”

Section: Introductionmentioning

confidence: 99%

“…Methods for dealing with class imbalance in machine learning can be divided into three groups, namely: data-level, algorithm-level, and hybrid approaches [11], [17]. Data-level methods aim to reduce class imbalance by adding new minority samples (i.e., oversampling) [13], removing redundant majority samples (i.e., undersampling) [14], or using a combination of both methods.…”

Section: Introductionmentioning

confidence: 99%

“…Data-level methods aim to reduce class imbalance by adding new minority samples (i.e., oversampling) [13], removing redundant majority samples (i.e., undersampling) [14], or using a combination of both methods. Algorithm-level methods are designed to adapt standard classification methods to emphasize learning from minority samples, improve the training mechanism or predicted rule [11]. In hybrid approaches, the developed algorithms modify both the distribution of unbalanced classes and the learning mechanism to classify unbalanced data [15].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Riyanto¹,

Sitanggang²,

Djatna³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Precision, Recall, and F1-score are metrics that are often used to evaluate model performance. Precision and Recall are very important to consider when the data is balanced, but in the case of unbalanced data the F1-score is the most important metric. To find out the importance of these metrics, a comparative analysis is needed in order to determine which metric is appropriate for the data being analyzed. This study aims to perform a comparative analysis of various evaluation metrics on unbalanced data in multi-class text classification. This study uses an unbalanced multi-class text dataset including: association, negative, cause of disease, and treatment of disease. This study involves five classifiers as algorithm-level approach, namely: Multinomial Naive Bayes, K-Nearest Neigbors, Support Vector Machine, Random Forest, and Long Short-Term Memory. Meanwhile, data-level approach, this study involves under sampling, over sampling, and synthetic minority oversampling technique. Several evaluation metrics used to evaluate model performance include Precision, Recall, and F1-score. The results show that the most suitable evaluation metric for use on unbalanced data depends on the purpose of use and the desired priority, including the classifier that is suitable for handling multi-class assignments on unbalanced data. The results of this study can assist practitioners in selecting evaluation metrics that are in accordance with the goals and application needs of multi-class text classification.

show abstract

Section: Class Imbalanced Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Riyanto¹,

Sitanggang²,

Djatna³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…This method uses DBSCAN to eliminate the synthesized noise instances after oversampling with SMOTE, thereby improving the classification performance of the model. Zhu et al 40 eliminated an instance interpolation oversampling method (OREM) by identifying candidate minority regions around minority instances, which can reliably expand minority regions without synthesizing noisy instances.…”

Section: Relate Workmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

View full text Add to dashboard Cite

One of the most widely used approaches to the class‐imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi‐armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi‐armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out‐of‐bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi‐armed bandit heterogeneous ensemble model is then assessed using 30 real‐world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state‐of‐the‐art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post‐hoc test.

show abstract

A train bearing imbalanced fault diagnosis method based on extended CCR and multi-scale feature fusion network

He,

Wei

et al. 2024

Nonlinear Dyn

View full text Add to dashboard Cite

Oversampling with Reliably Expanding Minority Class Regions for Imbalanced Data Learning

Cited by 8 publications

References 39 publications

Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

A train bearing imbalanced fault diagnosis method based on extended CCR and multi-scale feature fusion network

Contact Info

Product

Resources

About