A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An analysis of particle swarm optimizers
An analysis of particle swarm optimizers
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
A Rough Set Based Hybrid Method to Feature Selection
KAM '08 Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling
Particle swarm optimization based AdaBoost for face detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
PSO for feature construction and binary classification
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Critical interplay between density-dependent predation and evolution of the selfish herd
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
Feature selection (FS) is an important data preprocessing technique, which has two goals of minimising the classification error and minimising the number of features selected. Based on particle swarm optimisation (PSO), this paper proposes two multi-objective algorithms for selecting the Pareto front of non-dominated solutions (feature subsets) for classification. The first algorithm introduces the idea of non-dominated sorting based multi-objective genetic algorithm II into PSO for FS. In the second algorithm, multi-objective PSO uses the ideas of crowding, mutation and dominance to search for the Pareto front solutions. The two algorithms are compared with two single objective FS methods and a conventional FS method on nine datasets. Experimental results show that both proposed algorithms can automatically evolve a smaller number of features and achieve better classification performance than using all features and feature subsets obtained from the two single objective methods and the conventional method. Both the continuous and the binary versions of PSO are investigated in the two proposed algorithms and the results show that continuous version generally achieves better performance than the binary version. The second new algorithm outperforms the first algorithm in both continuous and binary versions.