Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach
@article{Xue2013ParticleSO, title={Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach}, author={Bing Xue and Mengjie Zhang and Will N. Browne}, journal={IEEE Transactions on Cybernetics}, year={2013}, volume={43}, pages={1656-1671} }
Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing…Â
Figures and Tables from this paper
782 Citations
New mechanism for archive maintenance in PSO-based multi-objective feature selection
- Computer ScienceSoft Comput.
- 2016
The experimental results show that in most cases, the proposed multi-objective algorithm generates better Pareto fronts than all other methods.
Feature Selection Using PSO: A Multi Objective Approach
- Computer ScienceICML 2020
- 2020
The enhanced form of NSPSOFS is presented, where a novel selection mechanism for gbest is incorporated and hybrid mutation is also added to the algorithms in order to generate a better pareto optimal front of non-dominated solutions.
Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification
- Computer ScienceIEEE/ACM Transactions on Computational Biology and Bioinformatics
- 2017
Experimental results show that the proposed PSO-based multi-objective feature selection algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.
Multimodal particle swarm optimization for feature selection
- Computer ScienceApplied Soft Computing
- 2021
Overview of Particle Swarm Optimisation for Feature Selection in Classification
- Computer ScienceSEAL
- 2014
The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms.
A novel multi population based particle swarm optimization for feature selection
- Computer ScienceKnowl. Based Syst.
- 2021
An archive based particle swarm optimisation for feature selection in classification
- Computer Science2014 IEEE Congress on Evolutionary Computation (CEC)
- 2014
A new PSO based feature selection approach, which introduces an external archive to store promising solutions obtained during the search process to guide the swarm to search for an optimal feature subset with the lowest classification error rate and a smaller number of features.
Feature selection for high-dimensional classification using a competitive swarm optimizer
- Computer ScienceSoft Comput.
- 2018
This paper proposes to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems, and demonstrates that compared to the canonical PSO-based and a state-of-the-art PSO variants for feature selection, the proposed CSO- based feature selection algorithm not only selects a much smaller number of features, but result in better classification performance as well.
Particle Swarm Optimisation for Feature Selection: A Size-Controlled Approach
- Computer ScienceAusDM
- 2015
The experimental results show that the proposed algorithm successfully further reduces the dimensionality of the dataset over original PSO and one of the conventional method, and maintains or even increases the classification performance in most cases.
A PSO-based multi-objective multi-label feature selection method in classification
- Computer ScienceScientific Reports
- 2017
This paper studies a multi-label feature selection algorithm using an improved multi-objective particle swarm optimization (PSO), with the purpose of searching for a Pareto set of non-dominated solutions (feature subsets).
References
SHOWING 1-10 OF 62 REFERENCES
New fitness functions in binary particle swarm optimisation for feature selection
- Computer Science2012 IEEE Congress on Evolutionary Computation
- 2012
Experimental results show that by using either of the two proposed fitness functions in the training process, in almost all cases, BPSO can select a smaller number of features and achieve higher classification accuracy on the test sets than using overall classification performance as the fitness function.
Feature subset selection by particle swarm optimization with fuzzy fitness function
- Computer Science2008 3rd International Conference on Intelligent System and Knowledge Engineering
- 2008
An algorithm based on particle swarm optimization with fuzzy fitness function has been proposed for getting optimal feature subset from a feature set with large number of features and is computationally less demanding in comparison to genetic algorithm.
Binary particle swarm optimisation for feature selection: A filter based approach
- Computer Science2012 IEEE Congress on Evolutionary Computation
- 2012
The results show that with proper weights, two proposed algorithms can significantly reduce the number of features and achieve similar or even higher classification accuracy in almost all cases.
An improved particle swarm optimization for feature selection
- Computer ScienceIntell. Data Anal.
- 2012
This paper designs a modified Multi-Swarm PSO (MSPSO) to solve discrete problems, and proposes an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method to achieve higher generalization capability.
Chaotic maps in binary particle swarm optimization for feature selection
- Computer Science2008 IEEE Conference on Soft Computing in Industrial Applications
- 2008
The chaotic binary particle swarm optimization method is proposed to implement feature selection, and the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier to evaluate the classification accuracies.
Feature selection based on rough sets and particle swarm optimization
- Computer SciencePattern Recognit. Lett.
- 2007
Improved binary particle swarm optimization using catfish effect for feature selection
- Computer ScienceExpert Syst. Appl.
- 2011
Adaptive Particle Swarm Optimizer for Feature Selection
- Computer ScienceIDEAL
- 2010
A new an adapted Particle Swarm Optimization for the exploration of the feature selection problem search space is proposed based on the original PSO formulation and integrates wrapper-filter methods within uniform framework.
Single Feature Ranking and Binary Particle Swarm Optimisation Based Feature Subset Ranking for Feature Selection
- Computer ScienceACSC
- 2012
Two wrapper based feature selection approaches, which are single feature ranking and binary particle swarm optimisation (BPSO) based feature subset ranking, are proposed and Experimental results show that using a relatively small number of the top-ranked features obtained from the first approach can achieve better classification performance than using all features.
A discrete particle swarm optimization method for feature selection in binary classification problems
- Computer ScienceEur. J. Oper. Res.
- 2010