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One of the applications of Rough set theory in machine learning is the so-called feature selection especially for classification problems. This is performed by means of finding a reduct set of attributes. Reduct set is a subset of all features which retains classification accuracy as original attributes.
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Feature Selection. Using Rough Sets Theory. Maciej Modrzejewski ... A measure of dependency of two sets of attributes P, R c_ Q is introduced in rough.
Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original ...
Rough set methods dedicated to feature selection are mainly based on algorithms for construction of reducts, and their different modifications. From the ...
We present applications of rough set methods for feature selection in pattern recognition. We emphasize the role of the basic constructs of rough set ...
The rough set selects the relevant features based on the FS algorithms, which improves the learning process of prediction methods [18,42].
Abstract—Feature selection techniques aim at reducing the number of unnecessary features in classification rules. The features are measured by their ...
Dec 29, 2021 · Abstract:This paper present a strong data mining method based on rough set, which can realize feature selection, classification and ...
We propose a new rough set based feature selection approach called Parameterized Average Support Heuristic (PASH). The PASH considers the overall quality of the ...
Abstract. Feature selection refers to the selection of input attributes that are most predictive of a given outcome. This is a problem encountered in many areas ...