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Indexed by:期刊论文

Date of Publication:2016-03-11

Journal:JOURNAL OF APPLIED STATISTICS

Included Journals:SCIE

Volume:43

Issue:4

Page Number:767-781

ISSN No.:0266-4763

Key Words:disaster; missing values; trapezoidal fuzzy numbers; K nearest neighbor

Abstract:Due to destructiveness of natural disasters, restriction of disaster scenarios and some human causes, missing data usually occur in disaster decision-making problems. In order to estimate missing values of alternatives, this paper focuses on imputing heterogeneous attribute values of disaster based on an improved K nearest neighbor imputation (KNNI) method. Firstly, some definitions of trapezoidal fuzzy numbers (TFNs) are introduced and three types of attributes (i.e. linguistic term sets, intervals and real numbers) are converted to TFNs. Then the correlated degree model is utilized to extract related attributes to form instances that will be used in K nearest neighbor algorithm, and a novel KNNI method merging with correlated degree model is presented. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and effectiveness.