Hybrid Marine Predator Algorithm with Simulated Annealing for Feature Selection

2022 ◽  
pp. 131-150
Author(s):  
Utkarsh Mahadeo Khaire ◽  
R. Dhanalakshmi ◽  
K. Balakrishnan
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Marine Predator

scholarly journals Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71943-71962 ◽  
Author(s):  
Heming Jia ◽  
Jinduo Li ◽  
Wenlong Song ◽  
Xiaoxu Peng ◽  
Chunbo Lang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Spotted Hyena

A Feature Selection Approach Based on Simulated Annealing for Detecting Various Denial of Service Attacks

2016 ◽  
Vol 2016 (1) ◽  
pp. 1-18 ◽  
Author(s):  
In-Seon Jeong ◽  
Hong-Ki Kim ◽  
Tae-Hee Kim ◽  
Dong Hwi Lee ◽  
Kuinam J. Kim ◽  
...  
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Selection Approach ◽  
Feature Selection Approach

Parameter determination of support vector machine and feature selection using simulated annealing approach

2008 ◽  
Vol 8 (4) ◽  
pp. 1505-1512 ◽  
Author(s):  
Shih-Wei Lin ◽  
Zne-Jung Lee ◽  
Shih-Chieh Chen ◽  
Tsung-Yuan Tseng
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Simulated Annealing ◽  
Parameter Determination ◽  
Support Vector

scholarly journals Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

2019 ◽  
Vol 8 (2) ◽  
pp. 910-916
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Mutation Operator ◽  
Knn Classifier ◽  
Optimal Feature Subset ◽  
Optimal Feature

The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

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