Outlier detection in neutrosophic sets by using rough entropy based weighted density method
作者机构：SCOPEVellore Institute of TechnologyVelloreIndia
出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)
年 卷 期：2020年第5卷第2期
页 面：121-127页
核心收录：
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：method. entropy incomplete
摘 要：Neutrosophy is the study of neutralities,which is an extension of discussing the truth of *** logic can be applied to any field,to provide the solution for indeterminacy *** of the real-world data have a problem of inconsistency,indeterminacy and *** sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information,but both concepts failed to handle indeterminate *** handle this complicated situation,researchers require a powerful mathematical tool,naming,neutrosophic sets,which is a generalised concept of fuzzy and intuitionistic fuzzy *** sets provide a solution for both incomplete and indeterminate *** has mainly three degrees of membership such as truth,indeterminacy and *** values are obtained from the three degrees of membership by cut relation *** items which contrast from other objects by their qualities are *** weighted density outlier detection method based on rough entropy calculates weights of each object and *** the obtained weighted values,the threshold value is fixed to determine *** analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.
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