Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics

2018 ◽  
Vol 31 (5) ◽  
pp. 1391-1403 ◽  
Author(s):  
Osama AlFarraj ◽  
Ahmad AlZubi ◽  
Amr Tolba
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Ant Colony Algorithm ◽  
Predictive Analytics ◽  
Ant Colony ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

A Hybrid Feature Selection Based on Ant Colony Optimization and Probabilistic Neural Networks for Bearing Fault Diagnostics

2007 ◽  
Vol 10-12 ◽  
pp. 573-577
Author(s):  
Y.H. Gai ◽  
Gang Yu
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Ant Colony Optimization ◽  
Feature Selection Method ◽  
Ant Colony ◽  
Diagnostic Purpose ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Probabilistic Neural Networks ◽  
Bearing Fault

This paper presents a novel hybrid feature selection algorithm based on Ant Colony Optimization (ACO) and Probabilistic Neural Networks (PNN). The wavelet packet transform (WPT) was used to process the bearing vibration signals and to generate vibration signal features. Then the hybrid feature selection algorithm was used to select the most relevant features for diagnostic purpose. Experimental results for bearing fault diagnosis have shown that the proposed hybrid feature selection method has greatly improved the diagnostic performance.

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