Feature Selection Metric Using AUC Margin for Small Samples and Imbalanced Data Classification Problems

2011 ◽  
Author(s):  
Malak Alshawabkeh ◽  
Javed A. Aslam ◽  
Jennifer Dy ◽  
David Kaeli
Keyword(s):  
Feature Selection ◽  
Imbalanced Data ◽  
Data Classification ◽  
Small Samples ◽  
Classification Problems ◽  
Imbalanced Data Classification

Evolutionary and Swarm-Based Feature Selection for Imbalanced Data Classification

2019 ◽  
pp. 231-250 ◽  
Author(s):  
Feras Namous ◽  
Hossam Faris ◽  
Ali Asghar Heidari ◽  
Monther Khalafat ◽  
Rami S. Alkhawaldeh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Imbalanced Data ◽  
Data Classification ◽  
Imbalanced Data Classification ◽  
Selection For

scholarly journals K-Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 2074-2079
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Classification Algorithms ◽  
Classification Problems ◽  
Imbalanced Classification ◽  
Imbalanced Data Classification ◽  
Traditional Classification ◽  
Boosting Method ◽  
Ensemble Algorithms

Imbalanced data classification is a critical and challenging problem in both data mining and machine learning. Imbalanced data classification problems present in many application areas like rare medical diagnosis, risk management, fault-detection, etc. The traditional classification algorithms yield poor results in imbalanced classification problems. In this paper, K-Means cluster based undersampling ensemble algorithm is proposed to solve the imbalanced data classification problem. The proposed method combines K-Means cluster based undersampling and boosting method. The experimental results show that the proposed algorithm outperforms the other sampling ensemble algorithms of previous studies.

scholarly journals K-Means Cluster Based Oversampling Algorithm for Imbalanced Data Classification

2020 ◽  
Vol 8 (5) ◽  
pp. 3436-3440
Keyword(s):  
Machine Learning ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
Skewed Data ◽  
Challenging Problem ◽  
Classification Problems ◽  
Imbalanced Data Classification

Imbalanced data classification problems endeavor to find a dependent variable in a skewed data distribution. Imbalanced data classification problems present in many application areas like, medical disease diagnosis, risk management, fault-detection, etc. It is a challenging problem in the field of machine learning and data mining. In this paper, K-Means cluster based oversampling algorithm is proposed to solve the imbalanced data classification problem. The experimental results show that the proposed algorithm outperforms the existing oversampling algorithms of previous studies.

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