Performing Feature Selection with ACO

R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006.

Rough set based feature selection : A review

Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone, requiring no additional information. This chapter describes the fundamental ideas behind RST-based approaches and reviews related feature selection methods that build on these ideas. Extensions to the traditional rough set approach are discussed, including recent selection methods based on tolerance rough sets, variable precision rough sets and fuzzy-rough sets. Alternative search mechanisms are also highly important in rough set feature selection. The chapter includes the latest developments in this area, including RST strategies based on hill-climbing, genetic algorithms and ant colony optimization.

Survey of Rough and Fuzzy Hybridization.

P. Lingras and R. Jensen, 'Survey of Rough and Fuzzy Hybridization,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 125-130, 2007.

Tolerance-based and Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007.

Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection,' Proceedings of the Fifth International Conference on Rough Sets and Current Trends in Computing (RSCTC 2006), LNAI 4259, pp. 147-156, 2006.

Finding Rough Set Reducts with SAT.

R. Jensen, Q. Shen and A. Tuson, 'Finding Rough Set Reducts with SAT,' Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNAI 3641, pp. 194-203, 2005.

Methods to accelerate the learning of bayesian network structures

R. Daly and Q. Shen. Methods to accelerate the learning of bayesian network structures. Proceedings of the Proceedings of the 2007 UK Workshop on Computational Intelligence.

Granularity and disaggregation in compositional modelling with applications to ecological systems

J. Keppens and Q. Shen. Granularity and disaggregation in compositional modelling with applications to ecological systems. Applied Intelligence, 25(3):269-292, 2006.

Data-driven fuzzy rule generation and its application for student academic performance evaluation

K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006.

Aiding classification of gene expression data with feature selection: a comparative study

C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: a comparative study. Computational Intelligence Research, 1(1):68-76.