# Imbalanced Data Classification Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/imbalanced-data-classification-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the challenges and techniques for handling imbalanced data in classification problems. It covers methods such as SMOTE, ROC analysis, cost-sensitive learning, ensemble methods, and their applications in fraud detection. The cluster also discusses the use of precision-recall and boosting algorithms, as well as the effectiveness of random forest in addressing imbalanced datasets. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11652 |
| Works | 97 |

## Topic papers all

Showing 15 of 97.

- [Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence](https://scholariq.org/papers/interpreting-black-box-models-a-review-on-explainable-artificial-intelligence/)
- [Facing Imbalanced Data--Recommendations for the Use of Performance Metrics](https://scholariq.org/papers/facing-imbalanced-data-recommendations-for-the-use-of-performance-metrics/)
- [Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare](https://scholariq.org/papers/heart-disease-identification-method-using-machine-learning-classification-in-e/)
- [A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms](https://scholariq.org/papers/a-hybrid-intelligent-system-framework-for-the-prediction-of-heart-disease-using/)
- [A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining](https://scholariq.org/papers/a-comparison-of-undersampling-oversampling-and-smote-methods-for-dealing-with/)
- [Credit Card Fraud Detection - Machine Learning methods](https://scholariq.org/papers/credit-card-fraud-detection-machine-learning-methods/)
- [Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model](https://scholariq.org/papers/automated-detection-and-classification-of-fundus-diabetic-retinopathy-images/)
- [A comparative analysis of techniques for predicting academic performance](https://scholariq.org/papers/a-comparative-analysis-of-techniques-for-predicting-academic-performance/)
- [Intelligent financial fraud detection practices in post-pandemic era](https://scholariq.org/papers/intelligent-financial-fraud-detection-practices-in-post-pandemic-era/)
- [A survey on statistical methods for health care fraud detection](https://scholariq.org/papers/a-survey-on-statistical-methods-for-health-care-fraud-detection/)
- [Vertical bagging decision trees model for credit scoring](https://scholariq.org/papers/vertical-bagging-decision-trees-model-for-credit-scoring/)
- [Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data](https://scholariq.org/papers/intelligent-machine-learning-approach-for-effective-recognition-of-diabetes-in-e/)
- [An Ensemble based Machine Learning model for Diabetic Retinopathy Classification](https://scholariq.org/papers/an-ensemble-based-machine-learning-model-for-diabetic-retinopathy-classification/)
- [New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM](https://scholariq.org/papers/new-imbalanced-bearing-fault-diagnosis-method-based-on-sample-characteristic/)
- [Neural network prediction analysis: The bankruptcy case](https://scholariq.org/papers/neural-network-prediction-analysis-the-bankruptcy-case/)

## Topic primary papers

Showing 15 of 27.

- [Facing Imbalanced Data--Recommendations for the Use of Performance Metrics](https://scholariq.org/papers/facing-imbalanced-data-recommendations-for-the-use-of-performance-metrics/)
- [A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining](https://scholariq.org/papers/a-comparison-of-undersampling-oversampling-and-smote-methods-for-dealing-with/)
- [Credit Card Fraud Detection - Machine Learning methods](https://scholariq.org/papers/credit-card-fraud-detection-machine-learning-methods/)
- [Intelligent financial fraud detection practices in post-pandemic era](https://scholariq.org/papers/intelligent-financial-fraud-detection-practices-in-post-pandemic-era/)
- [A survey on statistical methods for health care fraud detection](https://scholariq.org/papers/a-survey-on-statistical-methods-for-health-care-fraud-detection/)
- [New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM](https://scholariq.org/papers/new-imbalanced-bearing-fault-diagnosis-method-based-on-sample-characteristic/)
- [Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques](https://scholariq.org/papers/addressing-binary-classification-over-class-imbalanced-clinical-datasets-using/)
- [Federated learning model for credit card fraud detection with data balancing techniques](https://scholariq.org/papers/federated-learning-model-for-credit-card-fraud-detection-with-data-balancing/)
- [Few-shot imbalanced classification based on data augmentation](https://scholariq.org/papers/few-shot-imbalanced-classification-based-on-data-augmentation/)
- [Performance of Machine Learning Algorithms for Class-Imbalanced Process Fault Detection Problems](https://scholariq.org/papers/performance-of-machine-learning-algorithms-for-class-imbalanced-process-fault/)
- [Predicting Fraudulent Claims in Automobile Insurance](https://scholariq.org/papers/predicting-fraudulent-claims-in-automobile-insurance/)
- [RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System](https://scholariq.org/papers/rhsofs-feature-selection-using-the-rock-hyrax-swarm-optimization-algorithm-for/)
- [A Hybrid Approach for Credit Card Fraud Detection using Rough Set and Decision Tree Technique](https://scholariq.org/papers/a-hybrid-approach-for-credit-card-fraud-detection-using-rough-set-and-decision/)
- [Constrained Oversampling: An Oversampling Approach to Reduce Noise Generation in Imbalanced Datasets With Class Overlapping](https://scholariq.org/papers/constrained-oversampling-an-oversampling-approach-to-reduce-noise-generation-in/)
- [A machine learning approach to detecting fraudulent job types](https://scholariq.org/papers/a-machine-learning-approach-to-detecting-fraudulent-job-types/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.