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.

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.

Feature Selection based on Rough Sets and Particle Swarm Optimization

X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen. Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 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.

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.

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.

Application of Inductive Logic Programming to Structure-Based Drug Design

Enot, D. and King, R. D. (2003) Application of Inductive Logic Programming to Structure-Based Drug Design. 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD '03). Springer LNAI 2838 p156-167

Structure based drug design with inductive logic programming

David P. Enot and Ross D. King (2003). Structure based drug design with inductive logic programming. The ACS National Meeting Spring 2003, New Orleans