What question did this study set out to answer?

The aim is to extend rough topology concepts to numerical data and devise a new core determination method.

February 12, 2026Filomat1 citationsOpen Access

The rough topology for numerical data

UYUğur YiğitIstanbul Medeniyet University

Key Points

The aim is to extend rough topology concepts to numerical data and devise a new core determination method.
Generalized rough topology for numerical data
Introduced a measurement criterion for core attributes
Applied eight different machine-learning algorithms for testing
Provided algorithms and codes for data conversion and core determination
Demonstrated effective attribute reduction using the new core method
Ranked the importance of attributes in data classification
Showed comparable performance with existing machine-learning algorithms

Abstract

In this paper, we generalize the rough topology and the core to numerical data by classifying objects in terms of the attribute values. A new approach to finding the core for numerical data is discussed. A measurement criterion is introduced to determine whether an attribute belongs to the core. This new method for finding the core is used for attribute reduction. It is tested and compared by using eight different machine-learning algorithms. Also, it is discussed how this material is used to rank the importance of attributes in data classification. Finally, the algorithms and codes for data conversion and core determination are provided.

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Cite This Study

Uğur Yiğit (Wed,) studied this question.

synapsesocial.com/papers/698d6efe5be6419ac0d54f72 https://doi.org/https://doi.org/10.2298/fil2517019y

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