A New Under-Sampling Method Using Genetic Algorithm for Imbalanced Data Classification

2016 ◽  
Author(s):  
Jihyun Ha ◽  
Jong-Seok Lee
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Imbalanced Data ◽  
Data Classification ◽  
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 Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146116 ◽  
Author(s):  
Mohammad Nazmul Haque ◽  
Nasimul Noman ◽  
Regina Berretta ◽  
Pablo Moscato
Keyword(s):  
Genetic Algorithm ◽  
Imbalanced Data ◽  
Data Classification ◽  
Imbalanced Data Classification ◽  
Heterogeneous Ensemble
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