Subset selection using rough set in wavelet packet based texture classification
Qiong Wang, Li Hong, Jian Liu · 2008
Rough set based attribute significance measure and reduction is proposed in this paper, after we decompose textures using wavelet packet and extract the l1-norm as features, condition attributes are discretized with equal width binning method. We deduce the classification rules with the selected feature subset. The classification performance is tested on a set of 13 Brodatz texture, the averaged classification results show that the proposed algorithm can get rid of redundancy and only a few of the features can fulfill the classification task without reducing accuracy.