SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling

Guan, Hongjiao; Zhang, Yingtao; Xian, Min; Cheng, Heng-Da; Tang, Xianglong

doi:10.1007/s10489-020-01852-8

Cited by 66 publications

(27 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) Generate a set of Req-Reg. In order to make Req-Reg close to the real situation, we recommend obtaining Req-Reg directly from the number of daily citizen requests in each sub-region from historical records or integrating multiple historical records using the synthesized minority oversampling technique [70,71]. (2) Set the Arena environment according to the Req-Reg determined in Step 1.…”

Section: Training Data Set Of Gan Established By Arenamentioning

confidence: 99%

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

et al. 2022

View full text Add to dashboard Cite

As aging populations increase worldwide, many governments have introduced the concept of paratransit services to assist individuals with limited mobility with transportation. A successful paratransit service must be able to satisfy most requests to the system; this success is typically related to the allocation of vehicles to dispatch stations. A suitable configuration can reduce unnecessary travel time and thus serve more people. This resembles the classic Dial-a-Ride problem, which previous studies have solved using heuristic algorithms. Most of these algorithms, however, incur heavy computational costs and, therefore, cannot be operated online, especially when there are many conditions to consider, many configuration requirements, or many vehicles requested. Therefore, this paper proposes an approach based on the generative adversary network (GAN), which can reduce computation significantly. In online environments, this approach can be implemented in just a few seconds. Furthermore, the amount of computation is not affected by the number of conditions, configuration requirements, or vehicles requested. This approach is based on three important concepts: (1) designing a GAN to solve the target problem; (2) using an improved Voronoi diagram to divide the overall service area to generate the input of the GAN generator; (3) using well-known system simulation software Arena to swiftly generate many conditions for the target problem and their corresponding best solutions to train the GAN. The efficiency of the proposed approach was verified using a case study of paratransit services in Yunlin, Taiwan.

show abstract

Section: Training Data Set Of Gan Established By Arenamentioning

confidence: 99%

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Undersampling is a method of overcoming the data imbalance issue by randomly removing samples falling in a major class. The undersampling can save time for constructing a model by reducing the amount of data, but it has a disadvantage of losing information [20,29]. Oversampling is a method of overcoming the data imbalance issue by randomly replicating samples falling in a minor class [30].…”

Section: A Data-level Approach For Improving Classification Performance Of Imbalanced Datamentioning

confidence: 99%

Exploring Parkinson’s Disease Predictors based on Basic Intelligence Quotient and Executive Intelligence Quotient

Byeon¹

2021

IJACSA

View full text Add to dashboard Cite

It is important to identify the risk factors of dementia and prevent them for the health of patients and caregivers. This study (1) explored sampling methods that could minimize overfitting due to data imbalance using a data-level approach, (2) developed nine ensemble learning models for predicting Parkinson's Disease-Mild Cognitive Impairment (PD-MCI) ((undersampling, oversampling, and SMOTE) × (boosting, bagging, and random forest)=9), and (3) compared the accuracies, sensitivities, and specificities of these models to understand the prediction performance of the developed models. We examined 368 subjects: 320 healthy elderly people (≥60 and ≤74 years old) without Parkinson's disease (168 men and 152 women) and 48 subjects with PD-MCI (20 men and 28 women). This study used the Cognition Scale for Olde Adults (CSOA), which could measure cognitive functions comprehensively while considering age and education level, to determine the specific cognitive level of the subject. Our study developed nine prediction models ((undersampling, oversampling, and SMOTE) × (boosting, bagging, and random forest)=9) for developing a model to predict PD-MCI based on basic intelligence quotient and executive intelligence quotient. The analysis results showed that a random forest classifier with SMOTE had the best prediction performance with a sensitivity of 69.2%, a specificity of 75.7%, and a mean overall accuracy of 74.0%. In this final model, digit span test-backward, stroop test-interference trial, verbal memory test-delayed recall, verbal fluency test, and confrontation naming test were identified as the key variables with high weight in predicting PD-MCI. The results of this study implied that a random forest classifier with SMOTE could produce models with higher accuracy than a bagging classifier with SMOTE or a boosting classifier with SMOTE when analyzing imbalanced data.

show abstract

“…The proposed method improves performance by taking two actions: first, to reduce the dominance of the majority class by applying class decomposition, and second, to increase the representation of the minority class by oversampling. Moreover, a two-step hybridization of minority oversampling (SMOTE) and a novel data cleaning method (Weighted Edited Nearest Neighbor rule, or WENN) was proposed in [36]. Fajardo et al [37] have applied deep conditional generative models for learning to the distribution of minority classes and then generated synthetic samples for solving the class imbalance in the dataset to improve the model's performance.…”

Section: Adaptive Synthetic (Adasyn) Samplingmentioning

confidence: 99%

“…New sampling techniques based on deep learning and hybrid sampling approaches are proposed in the literature [35][36][37][38][39]. Future studies can explore these recent techniques with the power system and other critical infrastructure datasets.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

et al. 2021

View full text Add to dashboard Cite

Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.

show abstract

SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling

Cited by 66 publications

References 26 publications

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

Exploring Parkinson’s Disease Predictors based on Basic Intelligence Quotient and Executive Intelligence Quotient

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Contact Info

Product

Resources

About