Fusing Supervised and Unsupervised Measures for Attribute Reduction

It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which are insufficient to identify attributes with superior learning performance, such as stability and accuracy. In order to improve the classification stability and classification accuracy of reduct, in this paper, a novel measure is proposed based on the fusion of supervised and unsupervised perspectives: (1) in terms of supervised perspective, approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels; (2) in terms of unsupervised perspective, conditional entropy is helpful in quantitatively describing the internal structure of data itself. In order to prove the effectiveness of the proposed measure, 18 University of CaliforniaIrvine (UCI) datasets and 2 Yale face datasets have been employed in the comparative experiments. Finally, the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.