Multi-label Feature Selection Algorithm via Maximizing Label Correlation-Aware Relevance and Minimizing Redundance with Mutation Binary Particle Swarm Optimization

2021 ◽  
pp. 257-265
Author(s):  
Xiaolin Zhu ◽  
Yuanyuan Tao ◽  
Jun Li ◽  
Jianhua Xu
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization ◽  
Label Correlation

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

Feature selection algorithm based on bare bones particle swarm optimization

2015 ◽  
Vol 148 ◽  
pp. 150-157 ◽  
Author(s):  
Yong Zhang ◽  
Dunwei Gong ◽  
Ying Hu ◽  
Wanqiu Zhang
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization ◽  
Bare Bones

scholarly journals Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166066-166078 ◽  
Author(s):  
Bo Wei ◽  
Wensheng Zhang ◽  
Xuewen Xia ◽  
Yinglong Zhang ◽  
Fei Yu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

scholarly journals Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

2019 ◽  
Vol 27 (1) ◽  
pp. 171-183
Author(s):  
Ali Hakem Jabor ◽  
Ali Hussein Ali
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Selection Algorithm ◽  
Swarm Optimization

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization). 

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