Despite their diverse applications in many domains, the variable precision rough sets
(VPRS) model lacks a feasible method to determine a precision parameter (β) value to …

Most existing rough set-based feature selection algorithms suffer from intensive computation
of either discernibility functions or positive regions to find attribute reduct. In this paper, we …

Feature selection plays an important role in the classification problems associated with
expert and intelligent systems. The central idea behind feature selection is to identify …

The existing fuzzy rough set (FRS) models all believe that the decision attribute divides the
sample set into several “clear” decision classes, and this data processing method makes the …

The selection of features from data, as one of the most important tasks in data mining,
strongly affects the accuracy of classification. The removal of irrelevant and redundant …

Incremental feature selection approaches can improve the efficiency of feature selection
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …

Optimal granularity selection is a key issue in rough set, by which decision rules can be
generated to assign corresponding decision labels for new samples. The current evaluation …

Feature selection in the data with different types of feature values, ie, the heterogeneous or
mixed data, is especially of practical importance because such types of data sets widely …

Nowadays the incredibly advanced developments in information technologies have led to
exponential growth in the datasets with respect to both the dimensionality and the sample …

In this paper, we address the problem of feature subset selection using rough set theory. We
propose a scalable algorithm to find a set of reducts based on discernibility function, which is …