Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Pareto front feature selection: using genetic programming to explore feature space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Particle swarm optimization based AdaBoost for face detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Feature selection has two main objectives of maximising the classification performance and minimising the number of features. However, most existing feature selection algorithms are single objective wrapper approaches. In this work, we propose a multi-objective filter feature selection algorithm based on binary particle swarm optimisation (PSO) and probabilistic rough set theory. The proposed algorithm is compared with other five feature selection methods, including three PSO based single objective methods and two traditional methods. Three classification algorithms (naïve bayes, decision trees and k-nearest neighbours) are used to test the generality of the proposed filter algorithm. Experiments have been conducted on six datasets of varying difficulty. Experimental results show that the proposed algorithm can automatically evolve a set of non-dominated feature subsets. In almost all cases, the proposed algorithm outperforms the other five algorithms in terms of both the number of features and the classification performance (evaluated by all the three classification algorithms). This paper presents the first study on using PSO and rough set theory for multi-objective feature selection.