Application of Rough Set-Based Characterisation of Attributes in Feature Selection and Reduction

Stańczyk, Urszula

doi:10.1007/978-3-030-93052-3_3

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Thus, although constructs are commonly larger than reducts, they are a better option for building a reduced representation of a decision system preserving as much knowledge as possible. Constructs can be used to build rule-based classifiers [4], as feature selectors [5], in problems of reduction of the representation space of the objects [6], among others.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Computing All Rough Set Constructs for Dimensionality Reduction

González-Díaz,

Martínez-Trinidad,

Carrasco-Ochoa

et al. 2023

Mathematics

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In rough set theory, a construct is an attribute subset with the same ability to discern objects belonging to different classes as the whole set of attributes, while maintaining the similarity between objects belonging to the same class. Although algorithms for reducts computation can be adapted to compute constructs, practical problems exist where these algorithms cannot compute all constructs within a reasonable time frame. Therefore, this paper introduces an algorithm for computing all constructs of a decision system. The results of experiments with various decision systems (both artificial and real-world) suggest that our algorithm is, in most cases, faster than the state-of-the-art algorithms when the simplified binary discernibility–similarity matrix has a density of less than 0.29.

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Section: Introductionmentioning

confidence: 99%