A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation
| Autor: | Shiping Chen, Zhenquan Shi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Article Subject Computer science General Mathematics Information Theory Information Storage and Retrieval 02 engineering and technology lcsh:Computer applications to medicine. Medical informatics computer.software_genre lcsh:RC321-571 020901 industrial engineering & automation Diabetes Mellitus 0202 electrical engineering electronic engineering information engineering Humans Wu's method of characteristic set lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Principal Component Analysis General Neuroscience General Medicine Arbitrariness Weighting Knowledge lcsh:R858-859.7 A priori and a posteriori 020201 artificial intelligence & image processing Rough set Data mining computer Research Article |
| Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2018 (2018) |
| ISSN: | 1687-5265 |
| DOI: | 10.1155/2018/1838639 |
| Popis: | The knowledge characteristics weighting plays an extremely important role in effectively and accurately classifying knowledge. Most of the existing characteristics weighting methods always rely heavily on the experts’ a priori knowledge, while rough set weighting method does not rely on experts’ a priori knowledge and can meet the need of objectivity. However, the current rough set weighting methods could not obtain a balanced redundant characteristic set. Too much redundancy might cause inaccuracy, and less redundancy might cause ineffectiveness. In this paper, a new method based on rough set and knowledge granulation theories is proposed to ascertain the characteristics weight. Experimental results on several UCI data sets demonstrate that the weighting method can effectively avoid subjective arbitrariness and avoid taking the nonredundant characteristics as redundant characteristics. |
| Databáze: | OpenAIRE |
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