Iftikhar U Sikder
Cleveland State University, USA
Title: Application of Rough Sets and Dempster-Shafer's Evidence Theory in Spatial Data Mining
Biography
Biography: Iftikhar U Sikder
Abstract
This paper presents a novel approach to spatial classification and prediction of land cover classes using rough set and evidence theory. In particular, it presents an approach to characterizing uncertainty in multisource supervised classification problem. The evidential structure of spatial classification is founded on the notions of equivalence relations of rough set. It allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary region. A key advantage of this approach is that it allows incorporating the context of spatial neighborhood interactions and the combination of multiple spatial evidences. The empirical result demonstrates that the model classifier’s predictive performance significantly improves the accuracy of classification. A comparison of the predictive performance of the model with the radial basis function-based artificial neural network algorithm shows that the predictive performance of the proposed model is significantly better than neural network model.