Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
Probabilistic rough set approximations
International Journal of Approximate Reasoning
A Rough Set Based Hybrid Method to Feature Selection
KAM '08 Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling
Different metaheuristic strategies to solve the feature selection problem
Pattern Recognition Letters
Feature subset selection in large dimensionality domains
Pattern Recognition
Particle swarm optimization based AdaBoost for face detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improved binary particle swarm optimization using catfish effect for feature selection
Expert Systems with Applications: An International Journal
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
A multi-objective feature selection approach based on binary PSO and rough set theory
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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Dimension reduction aims to remove unnecessary attributes from datasets to overcome the problem of "the curse of dimensionality", which is an obstacle in classification. Based on the analysis of the limitations of the standard rough set theory, we propose a new dimension reduction approach based on binary particle swarm optimisation (BPSO) and probabilistic rough set theory. The new approach includes two new specific algorithms, which are PSOPRS using only the probabilistic rough set in the fitness function and PSOPRSN adding the number of attributes in the fitness function. Decision trees, naive Bayes and nearest neighbour algorithms are employed to evaluate the classification accuracy of the reduct achieved by the proposed algorithms on five datasets. Experimental results show that the two new algorithms outperform the algorithm using BPSO with standard rough set and two traditional dimension reduction algorithms. PSOPRSN obtains a smaller number of attributes than PSOPRS with the same or slightly worse classification performance. This work represents the first study on probabilistic rough set for for filter dimension reduction in classification problems.