Classification of Non-Performing Financing Using Logistic Regression and Synthetic Minority Over-sampling Technique-Nominal Continuous (SMOTE-NC)

doi:10.15849/ijasca.211128.09

Cited by 5 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, SVM-SMOTE (Tang et al, 2009) was the best oversampling method in both datasets. Wongvorachan et al (2023) selected Random Oversampling, Random Undersampling, and a combination of SMOTE-NC (Nominal and Continuous) (Islahulhaq et al, 2021) and Random Undersampling to compare its improvements on the High School Longitudinal Study dataset. The study adopted Random Forest as the classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing AutoML Performance for Imbalanced Tabular Data Classification: A Self-Balancing Pipeline

Aragão,

Carvalho,

Pereira

et al. 2024

Preprint

View full text Add to dashboard Cite

In data science and machine learning, imbalanced data poses a significant challenge. This study presents a self-balancing strategy integrating traditional (randomly duplicating data from the minority class) and generative (creating novel samples from the minority class' features space oversampling, undersampling techniques, and hyperparameter optimization to enhance automated machine-learning pipelines. Through a systematic grid search methodology and by taking multiple datasets into account, the research validates the effectiveness of integrated sampling techniques in consistently improving classification performance in diverse scenarios. The proposed approach contributes to a refined understanding of addressing class imbalance, emphasizing the importance of a unified strategy. Therefore, this research presents a comprehensive proposal for effectively handling imbalanced datasets and advancing the development of more reliable machine learning applications. The experiments' results highlighted the effectiveness of sampling methods and showed improvements of up to almost six percentage points compared to the baseline. We made the code, experiment reproduction instructions, and outcomes publicly available on GitHub.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Enhancing AutoML Performance for Imbalanced Tabular Data Classification: A Self-Balancing Pipeline

Aragão,

Carvalho,

Pereira

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To address this class imbalance, we employed a hybrid resampling technique. Specifically, we implemented the Synthetic Minority Oversampling Technique for nominal and categorical data (SMOTE-NC) and random undersampling (RUS) to increase minority cases and decrease majority cases [62,63]. The SMOTE-NC algorithm synthesized 80% of the minority cases, while the RUS algorithm undersampled non-minority cases to match the number of minority cases.…”

Section: Dataset and Data Preprocessingmentioning

confidence: 99%

A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning

Wongvorachan,

Bulut,

Liu

et al. 2024

Information

View full text Add to dashboard Cite

Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation.

show abstract

“…This study employs the synthetic minority oversampling technique-nominal continuous as suggested by Chawla et al [14] since it balances a dataset by handling continuous and categorical features simultaneously. SMOTE-NC is chosen over other data balancing techniques because it is simple, computationally efficient, and has exceptional performance as shown by different researchers such as Rahmayanti et al [25] and Islahulhaq and Ratih [26] in their studies. In addition, it's also worth mentioning that SMOTE-NC works with any classification approach since it is independent of the classifier.…”

Section: Synthetic Minority Oversampling Technique-nominal Continuous...mentioning

confidence: 99%

“…However, SMOTE-NC is intended for datasets with both categorical and numerical features; it's not well suited for datasets with categorical features only [14]. Islahulhaq and Ratih [26] notes that a dataset can be categorised as imbalanced if the cases of minority class are less than 35% of cases from the majority class, that is, if the imbalance ratio is less than 0.35.…”

Section: Synthetic Minority Oversampling Technique-nominal Continuous...mentioning

confidence: 99%

“…From the results in Table 1 and the plots in Figure 7 and Figure 8, it is clear that the datasets are highly imbalanced with all imbalance ratios less than 0.35the threshold of a dataset to be classified as imbalanced as per Islahulhaq and Ratih [26]. Synthetic minority oversampling technique-nominal continuous (SMOTE-NC) is thus employed to balance the datasets by generating synthetic data points for the minority class (attrition).…”

Section: Data Balancingmentioning

confidence: 99%

See 1 more Smart Citation

Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous

Kahacho¹,

Wamwea²,

Malenje³

et al. 2023

JDAIP

View full text Add to dashboard Cite

HIV and AIDS has continued to be a major public health concern, and hence one of the epidemics that the world resolved to end by 2030 as highlighted in sustainable development goals (SDGs). A colossal amount of effort has been taken to reduce new HIV infections, but there are still a significant number of new infections reported. HIV prevalence is more skewed towards the key population who include female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID). The study design was retrospective and focused on key population enrolled in a comprehensive HIV and AIDS programme by the Kenya Red Cross Society from July 2019 to June 2021. Individuals who were either lost to follow up, defaulted (dropped out, transferred out, or relocated) or died were classified as attrition; while those who were active and alive by the end of the study were classified as retention.The study used density analysis to determine the spatial differences of key population attrition in the 19 targeted counties, and used Kilifi county as an example to map attrition cases in smaller administrative areas (sub-county level). The study used synthetic minority oversampling technique-nominal continuous (SMOTE-NC) to balance the datasets since the cases of attrition were much less than retention. The random survival forests model was then fitted to the balanced dataset. The model correctly identified attrition cases using the predicted ensemble mortality and their survival time using the estimated Kaplan-Meier survival function. The predictive performance of the model was strong and way better than random chance with concordance indices greater than 0.75.

show abstract

Classification of Non-Performing Financing Using Logistic Regression and Synthetic Minority Over-sampling Technique-Nominal Continuous (SMOTE-NC)

Cited by 5 publications

References 8 publications

Enhancing AutoML Performance for Imbalanced Tabular Data Classification: A Self-Balancing Pipeline

Enhancing AutoML Performance for Imbalanced Tabular Data Classification: A Self-Balancing Pipeline

A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning

Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous

Contact Info

Product

Resources

About