Swarm intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization
Expert Systems with Applications: An International Journal
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
AMPSO: a new particle swarm method for nearest neighborhood classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fractional particle swarm optimization in multidimensional search space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A decision support system based on metaheuristic model for aircrafts landing problems
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
Optimal training subset in a support vector regression electric load forecasting model
Applied Soft Computing
A hybrid meta-heuristic algorithm for optimization of crew scheduling
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.00 |
Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary objective is to remove irrelevant features in the feature space and to correctly identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully to solving feature selection problems. In this paper, two kinds of chaotic maps-so-called logistic maps and tent maps-are embedded in BPSO. The purpose of chaotic maps is to determine the inertia weight of the BPSO. We propose chaotic binary particle swarm optimization (CBPSO) to implement the feature selection, in which the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier for evaluating classification accuracies. The proposed feature selection method shows promising results with respect to the number of feature subsets. The classification accuracy is superior to other methods from the literature.