Online Multi-label Feature Selection on Imbalanced Data Sets

2018 ◽  
pp. 165-174
Author(s):  
Jing Liu ◽  
Zhongwen Guo ◽  
Zhongwei Sun ◽  
Shiyong Liu ◽  
Xupeng Wang
Keyword(s):  
Feature Selection ◽  
Imbalanced Data ◽  
Data Sets ◽  
Imbalanced Data Sets

FEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETS

2012 ◽  
Vol 20 (03) ◽  
pp. 369-397 ◽  
Author(s):  
PEDRO VILLAR ◽  
ALBERTO FERNÁNDEZ ◽  
RAMÓN A. CARRASCO ◽  
FRANCISCO HERRERA
Keyword(s):  
Feature Selection ◽  
Statistical Tests ◽  
Imbalanced Data ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Data Sets ◽  
Rule Based ◽  
Imbalanced Data Sets ◽  
Wide Range ◽  
Fuzzy Labels

This paper proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact models by selecting the adequate variables and adapting the number of fuzzy labels for each problem, improving the interpretability of the model. The experimental analysis is carried out over a wide range of highly imbalanced data-sets and uses the statistical tests suggested in the specialized literature.

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.

Sign in / Sign up

Export Citation Format

Share Document