A New Hybrid Binary Algorithm of Bat Algorithm and Differential Evolution for Feature Selection and Classification

2020 ◽  
pp. 1-18
Author(s):  
Abdelmonem M. Ibrahim ◽  
Mohamed A. Tawhid
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Bat Algorithm

scholarly journals Feature Selection Based on Modified Bat Algorithm

2017 ◽  
Vol E100.D (8) ◽  
pp. 1860-1869 ◽  
Author(s):  
Bin YANG ◽  
Yuliang LU ◽  
Kailong ZHU ◽  
Guozheng YANG ◽  
Jingwei LIU ◽  
...  
Keyword(s):  
Feature Selection ◽  
Bat Algorithm ◽  
Modified Bat Algorithm

scholarly journals Binary Bat Algorithm for text feature selection in news events detection model using Markov clustering

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Wafa Zubair Al-Dyani ◽  
Farzana Kabir Ahmad ◽  
Siti Sakira Kamaruddin
Keyword(s):  
Feature Selection ◽  
Bat Algorithm ◽  
Detection Model ◽  
Text Feature ◽  
Markov Clustering ◽  
News Events ◽  
Events Detection

scholarly journals Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668529 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Bat Algorithm ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Machine Method ◽  
Training Samples ◽  
Kernel Parameter

In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.

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