A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set

Variable Precision Rough Set Model

A general ized model of rough sets called variable precision model (VP-model), a imed at modell ing classification problems involving uncertain or imprecise information, is presented. The general ized model inherits all basic mathematical propert ies of the original model introduced by Pawlak. The main concepts are introduced formally and illustrated with simple examples. The application of the...

Variable-precision dominance-based rough set approach and attribute reduction

In this paper, a variable-precision dominance-based rough set approach (VP-DRSA) is proposed together with several VP-DRSA-based approaches to attribute reduction. The properties of VP-DRSA are shown in comparison to previous dominance-based rough set approaches. An advantage of VP-DRSA over variable-consistency dominance-based rough set approach in decision rule induction is emphasized. Some r...

Classification Using the Variable Precision Rough Set

In this paper, we present a new version of discernibility matrix, so called variable precision discernibility matrix, which can tolerate the noise of information, in addition, a reduction algorithm is also presented based on the partial order relation of the conditional attribute and used to do image classification. Compared with traditional rough set and BP network, the results will be much be...

A Variable Precision Covering-Based Rough Set Model Based on Functions

Classical rough set theory is a technique of granular computing for handling the uncertainty, vagueness, and granularity in information systems. Covering-based rough sets are proposed to generalize this theory for dealing with covering data. By introducing a concept of misclassification rate functions, an extended variable precision covering-based rough set model is proposed in this paper. In a...

Proposed Improved Bee Colony Optimization Based on Rough Set Algorithm