Classification with Dynamic Reducts and Belief Functions

In this paper, we propose two approaches of classification namely, Dynamic Belief Rough Set Classifier (D-BRSC) and Dynamic Belief Rough Set Classifier based on Generalization Distribution Table (D-BRSC-GDT). Both the classifiers are induced from uncertain data to generate classification rules. The uncertainty appears only in decision attribute values and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. D-BRSC only uses the basic concepts of Rough Sets (RS). However, D-BRSC-GDT is based on GDT-RS which is a hybridization of Generalization Distribution Table (GDT) and Rough Sets (RS). The feature selection step relative to the construction of the two classifiers uses the approach of dynamic reduct which extracts more relevant and stable features. The reduction of uncertain and noisy decision table using dynamic approach generates more significant decision rules for the classification of unseen objects. To prove that, we carry experimentations on real databases according to three evaluation criteria including the classification accuracy. We also compare the results of D-BRSC and D-BRSC-GDT with those obtained from Static Belief Rough Set Classifier (S-BRSC) and Static Belief Rough Set Classifier based on Generalization Distribution Table (S-BRSC-GDT). To further evaluate our rough sets based classification systems, we compare our results with those obtained from the Belief Decision Tree (BDT).