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Missing value imputation method for disaster decision-making using K nearest neighbor

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  • Xiaofei Ma
  • Qiuyan Zhong

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.

Suggested Citation

  • Xiaofei Ma & Qiuyan Zhong, 2016. "Missing value imputation method for disaster decision-making using K nearest neighbor," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 767-781, March.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:767-781
    DOI: 10.1080/02664763.2015.1077377
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    1. Cook, Wade D. & Harrison, Julie & Rouse, Paul & Zhu, Joe, 2012. "Relative efficiency measurement: The problem of a missing output in a subset of decision making units," European Journal of Operational Research, Elsevier, vol. 220(1), pages 79-84.
    2. Doove, L.L. & Van Buuren, S. & Dusseldorp, E., 2014. "Recursive partitioning for missing data imputation in the presence of interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 92-104.
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