Origin of avian genome size and structure in non-avian dinosaurs

Chris L. Organ, Andrew M. Shedlock, Andrew Meade, Mark Pagel and Scott V. Edwards (2007). Origin of avian genome size and structure in non-avian dinosaurs. Nature, 46(7132), 180-184. RAE2008

Drainage induced convection rolls in foams

Hutzler, S., Cox, S.J., Janiaud, E. and Weaire, D. (2007) Drainage induced convection rolls in foams. Colloids and Surfaces A: Physicochemical and Engineering Aspects Volume 309, Issues 1-3, 1 November 2007, Pages 33-37 A Collection of Papers Presented at the 6th Eufoam Conference, Potsdam, Germany, 2-6 July, 2006 Sponsorship: European Space Agency (14914/02/NL/SH, 14308/00/NL/SG) (AO-99-031) CCN 002 MAP Project AO-99-075); Science Foundation Ireland (RFP 05/RFP/PHY0016); Royal Society; UWA Learned Societies.

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

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.