Rough sets, their extensions and applications

Q. Shen and R. Jensen, 'Rough sets, their extensions and applications,' International Journal of Automation and Computing (IJAC), vol. 4, no. 3, pp. 217-218, 2007.

Customers' responses to retail brand extensions

Alexander, N.; and Colgate, N. (2005). Customers' responses to retail brand extensions. Journal of Marketing Management. 21(3-4), pp.393-419 RAE2008

Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules

Gohm, Rolf; Skeide, M., (2005) 'Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules', Infinite Dimensional Analysis, Quantum Probability and Related Topics 8(2) pp.291-305 RAE2008

Predicting gene function in Saccharomyces cerevisiae

Clare, A. and King R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. 2nd European Conference on Computational Biology (ECCB '03). (published as a journal supplement in Bioinformatics 19: ii42-ii49)

Interaction between coronal mass ejections and the solar wind

Jones, R. A.; Breen, A. R.; Fallows, R. A.; Canals, A.; Bisi, M. M.; Lawrence, G. (2007). Interaction between coronal mass ejections and the solar wind, Journal of Geophysical Research, 112, Issue A8 RAE2008

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.

Using ant colony optimisation in learning Bayesian network equivalence classes

R. Daly, Q. Shen and S. Aitken. Using ant colony optimisation in learning Bayesian network equivalence classes. Proceedings of the 2006 UK Workshop on Computational Intelligence, pages 111-118.

Boundary triplets and M-functions for non-selfadjoint operators, with applications to elliptic PDEs and block operator matrices

B.M. Brown, M. Marletta, S. Naboko, I. Wood: Boundary triplets and M-functions for non-selfadjoint operators, with applications to elliptic PDEs and block operator matrices, J. London Math. Soc., June 2008; 77: 700-718. The full text of this article will be made available in this repository in June 2009 Sponsorship: EPSRC,INTAS