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… 

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References

SHOWING 1-10 OF 62 REFERENCES
New fitness functions in binary particle swarm optimisation for feature selection
TLDR
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
  • B. Chakraborty
  • Computer Science
    2008 3rd International Conference on Intelligent System and Knowledge Engineering
  • 2008
TLDR
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
TLDR
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
TLDR
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
TLDR
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.
Improved binary particle swarm optimization using catfish effect for feature selection
Adaptive Particle Swarm Optimizer for Feature Selection
TLDR
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
TLDR
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
...
...