Enhancing Synthetic Oversampling for Imbalanced Datasets Using Proxima-Orion Neighbors and q-Gaussian Weighting Technique
In this article, we propose a novel oversampling algorithm to increase the number of instances of minority class in an imbalanced dataset. We select two instances, Proxima and Orion, from the set of all minority class instances, based on a combination of relative distance weights and density estimation of majority class instances. Furthermore, the q-Gaussian distribution is used as a weighting mechanism to produce new synthetic instances to improve the representation and diversity. We conduct a comprehensive experiment on 42 datasets extracted from KEEL software and eight datasets from the UCI ML repository to evaluate the usefulness of the proposed (PO-QG) algorithm. Wilcoxon signed-rank test is used to compare the proposed algorithm with five other existing algorithms. The test results show that the proposed technique improves the overall classification performance. We also demonstrate the PO-QG algorithm to a dataset of Indian patients with sarcopenia.
Code (0)
등록된 구현이 없습니다.
Tasks
Density EstimationDiversityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classification
With the abundance of industrial datasets, imbalanced classification has become a common problem in several application domains. Oversampling is an effective method to solve imbalanced classification. One of the main cha…
ClassificationGeneral Classificationimbalanced classificationVocal Bursts Intensity Prediction+1GenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets
Imbalanced datasets are ubiquitous. Classification performance on imbalanced datasets is generally poor for the minority class as the classifier cannot learn decision boundaries well. However, in sensitive applications l…
Fraud DetectionMedical DiagnosisINGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification
In classification problems, the datasets are usually imbalanced, noisy or complex. Most sampling algorithms only make some improvements to the linear sampling mechanism of the synthetic minority oversampling technique (S…
Anchor link predictionDiversityimbalanced classificationDeep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification
Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to addr…
imbalanced classificationWOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning
Machine learning classifiers often stumble over imbalanced datasets where classes are not equally represented. This inherent bias towards the majority class may result in low accuracy in labeling minority class. Imbalanc…
Fraud DetectionGeneral ClassificationIntrusion DetectionNetwork Intrusion Detection