scholarly journals A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data

2012 ◽  
Vol 38 ◽  
pp. 27-31 ◽  
Author(s):  
Barnali Sahu ◽  
Debahuti Mishra
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Microarray Data ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

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.

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
Sign in / Sign up

Export Citation Format

Share Document