A new classification method based on rough sets theory
| Autor: | Sedat Telceken, Rasim Çekik |
|---|---|
| Přispěvatelé: | Anadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Similarity (geometry) Computational intelligence 02 engineering and technology computer.software_genre Theoretical Computer Science Matrix (mathematics) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Data Mining Mathematics business.industry Dominance-based rough set approach Pattern recognition Object (computer science) Classification Rough Sets Theory (Rst) Classification methods 020201 artificial intelligence & image processing Geometry and Topology Data mining Artificial intelligence Rough set business computer Software |
| ISSN: | 0004-2676 |
| Popis: | WOS: 000426761200012 Discovering the common attributes of an object is an important problem in classification. The rough sets theory (RST) successfully reveals the relationship between an object, its attributes and classes and helps bring a solution to the classification problem. In this study, a new classification method has been developed that uses RST and a similarity-based method to create the weight matrix scoring system. The proposed method is named feature weighted rough set classification (FWRSC) and is compared with the classification methods in WEKA for five different datasets. The experimental results show that FWRSC gives higher performance than most of the methods in WEKA. Additionally, FWRSC produces the highest performance in terms of accuracy with an overall average of 67.47% for five different datasets. Anadolu University Scientific Research Project Commission [1402F047] This work has been partially supported by Anadolu University Scientific Research Project Commission under the Grant Number 1402F047. |
| Databáze: | OpenAIRE |
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