An Under-Sampling Method with Support Vectors in Multi-class Imbalanced Data Classification

2019 ◽  
Author(s):  
Md. Yasir Arafat ◽  
Sabera Hoque ◽  
Shuxiang Xu ◽  
Dewan Md. Farid
Keyword(s):  
Sampling Method ◽  
Imbalanced Data ◽  
Data Classification ◽  
Support Vectors ◽  
Imbalanced Data Classification ◽  
Under Sampling

scholarly journals CoGBUS- Center of Gravity based under Sampling Method for Imbalanced Data Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 2463-2468
Keyword(s):  
Learning Community ◽  
Sampling Method ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Center Of Gravity ◽  
Classification Algorithms ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Imbalanced Data Classification ◽  
Under Sampling

Learning of class imbalanced data becomes a challenging issue in the machine learning community as all classification algorithms are designed to work for balanced datasets. Several methods are available to tackle this issue, among which the resampling techniques- undersampling and oversampling are more flexible and versatile. This paper introduces a new concept for undersampling based on Center of Gravity principle which helps to reduce the excess instances of majority class. This work is suited for binary class problems. The proposed technique –CoGBUS- overcomes the class imbalance problem and brings best results in the study. We take F-Score, GMean and ROC for the performance evaluation of the method.

scholarly journals A novel imbalanced data classification approach using both under and over sampling

2021 ◽  
Vol 10 (5) ◽  
pp. 2789-2795
Author(s):  
Seyyed Mohammad Javadi Moghaddam ◽  
Asadollah Noroozi
Keyword(s):  
Sampling Methods ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Classification ◽  
Processing Technique ◽  
Imbalanced Dataset ◽  
Minority Class ◽  
Imbalanced Data Classification ◽  
Under Sampling ◽  
Sampling Algorithms

The performance of the data classification has encountered a problem when the data distribution is imbalanced. This fact results in the classifiers tend to the majority class which has the most of the instances. One of the popular approaches is to balance the dataset using over and under sampling methods. This paper presents a novel pre-processing technique that performs both over and under sampling algorithms for an imbalanced dataset. The proposed method uses the SMOTE algorithm to increase the minority class. Moreover, a cluster-based approach is performed to decrease the majority class which takes into consideration the new size of the minority class. The experimental results on 10 imbalanced datasets show the suggested algorithm has better performance in comparison to previous approaches.

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