Evolutionary Multitasking for Feature Selection in High-dimensional Classification via Particle Swarm Optimisation

2021 ◽  
pp. 1-1
Author(s):  
Ke Chen ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
Fengyu Zhou
Keyword(s):  
Feature Selection ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
High Dimensional ◽  
Dimensional Classification ◽  
Evolutionary Multitasking

scholarly journals Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering

2020 ◽  
Author(s):  
D O'Neill ◽  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Fitness Function ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Particle Swarm ◽  
Learning Task ◽  
Particle Swarm Optimisation ◽  
Feature Weighting ◽  
High Dimensional ◽  
Validation Measure

© 2018 IEEE. Clustering, an important unsupervised learning task, is very challenging on high-dimensional data, since the generated clusters can be significantly less meaningful as the number of features increases. Feature selection and/or feature weighting can address this issue by selecting and weighting only informative features. These techniques have been extensively studied in supervised learning, e.g. classification, but they are very difficult to use with clustering due to the lack of effective similarity/distance and validation measures. This paper utilises the powerful global search ability of particle swarm optimisation (PSO) on continuous problems, to propose a PSO based method for simultaneous feature selection and feature weighting for clustering on high-dimensional data, where a new validation measure is also proposed as the fitness function of the PSO method. Experiments on datasets with varying dimensionalities and different number of known clusters show that the proposed method can successfully improve clustering performance of different types of clustering algorithms over using the baseline of the original feature set.

scholarly journals Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering

2020 ◽  
Author(s):  
D O'Neill ◽  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Fitness Function ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Particle Swarm ◽  
Learning Task ◽  
Particle Swarm Optimisation ◽  
Feature Weighting ◽  
High Dimensional ◽  
Validation Measure

© 2018 IEEE. Clustering, an important unsupervised learning task, is very challenging on high-dimensional data, since the generated clusters can be significantly less meaningful as the number of features increases. Feature selection and/or feature weighting can address this issue by selecting and weighting only informative features. These techniques have been extensively studied in supervised learning, e.g. classification, but they are very difficult to use with clustering due to the lack of effective similarity/distance and validation measures. This paper utilises the powerful global search ability of particle swarm optimisation (PSO) on continuous problems, to propose a PSO based method for simultaneous feature selection and feature weighting for clustering on high-dimensional data, where a new validation measure is also proposed as the fitness function of the PSO method. Experiments on datasets with varying dimensionalities and different number of known clusters show that the proposed method can successfully improve clustering performance of different types of clustering algorithms over using the baseline of the original feature set.

scholarly journals Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1816
Author(s):  
Hailun Xie ◽  
Li Zhang ◽  
Chee Peng Lim ◽  
Yonghong Yu ◽  
Han Liu
Keyword(s):  
Feature Selection ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Particle Swarm Optimisation ◽  
Data Sets ◽  
Discriminative Feature ◽  
Advanced Search ◽  
Selection For ◽  
Selection Tasks ◽  
New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

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.

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