I'm using the rough sets (reducts) for feature selection. I took a decision to work on the filter approach because it is independent of any learning classifier, I read some of the algorithms as RSAR "rough set attribute reduction"- 2004, VPRS "variable precision rough sets - 1993" and "Han et. al. 2004"
But I'm wondering is there any new algorithms rather than those previously mentioned?
Hope the below article will help..
1. Hu, Q., Yu, D., Liu, J., Wu, C., 2008. Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 178, 3577–3594. doi:10.1016/j.ins.2008.05.024
2. Jensen, R., Shen, Q., 2009. New Approaches to Fuzzy-Rough Feature Selection. IEEE Trans. Fuzzy Syst. 17, 824–838. doi:10.1109/TFUZZ.2008.924209
3. Mishra, D., Rath, A.K., Acharya, M., Jena, T., 2009. Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data. Int J Comput. Commun. Technol. 1, 85–98.
4.Swiniarski, R.W., Skowron, A., 2003. Rough set methods in feature selection and recognition. Pattern Recognit. Lett. 24, 833–849. doi:10.1016/S0167-8655(02)00196-4
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