On the feature extraction in discrete space
Künye
Yıldız, O. T. (2014). On the feature extraction in discrete space. Pattern Recognition, 47(5), 1988-1993. doi:10.1016/j.patcog.2013.11.023Özet
In many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values of k discrete attributes exhaustively, (ii) modify the well-known decision tree and rule induction classifiers (ID3, Quilan, 1986 [1] and Ripper, Cohen, 1995 [2]) using these orderings as the new attributes. Our simulation results on 15 datasets from UCI repository [3] show that the novel classifiers perform better than the proper ones in terms of error rate and complexity.