A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets

2016 ◽  
Author(s):  
Romero F.A.B. de Morais ◽  
Pericles B.C. Miranda ◽  
Ricardo M.A. Silva
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Learning Method ◽  
Imbalanced Data Sets ◽  
Meta Learning ◽  
Under Sampling ◽  
Sampling Algorithms

scholarly journals Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 2122-2133 ◽  
Author(s):  
Christos K. Aridas ◽  
Stamatis Karlos ◽  
Vasileios G. Kanas ◽  
Nikos Fazakis ◽  
Sotiris B. Kotsiantis
Keyword(s):  
Naive Bayes ◽  
Imbalanced Data ◽  
Naïve Bayes ◽  
Data Sets ◽  
Imbalanced Data Sets ◽  
Under Sampling

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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