A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation. (31st May 2018)

• Record Type: Journal Article
• Authors: Shi, Zhenquan; Chen, Shiping
• Other Names: Gastaldo Paolo Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2018(2018)
• Publication Date: 2018-05-31
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2018/1838639
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.