Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Autor: Tamilarasu Sangeetha, Geetha Mary Amalanathan
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: CAAI Transactions on Intelligence Technology (2020)
Druh dokumentu: article
ISSN: 2468-2322
DOI: 10.1049/trit.2019.0093
Popis: Neutrosophy is the study of neutralities, which is an extension of discussing the truth of opinions. Neutrosophic logic can be applied to any field, to provide the solution for indeterminacy problem. Many of the real-world data have a problem of inconsistency, indeterminacy and incompleteness. Fuzzy sets provide a solution for uncertainties, and intuitionistic fuzzy sets handle incomplete information, but both concepts failed to handle indeterminate information. To handle this complicated situation, researchers require a powerful mathematical tool, naming, neutrosophic sets, which is a generalised concept of fuzzy and intuitionistic fuzzy sets. Neutrosophic sets provide a solution for both incomplete and indeterminate information. It has mainly three degrees of membership such as truth, indeterminacy and falsity. Boolean values are obtained from the three degrees of membership by cut relation method. Data items which contrast from other objects by their qualities are outliers. The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute. From the obtained weighted values, the threshold value is fixed to determine outliers. Experimental 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.
Databáze: Directory of Open Access Journals
Externí odkaz: