An Improvement of FSDD for Evaluating Multi-Dimensional Data

다차원 데이터 평가가 가능한 개선된 FSDD 연구

  • Oh, Se-jong (Dept. of Software Science, Dankook University)
  • 오세종 (단국대학교 공과대학 소프트웨어학과)
  • Received : 2016.11.22
  • Accepted : 2017.01.20
  • Published : 2017.01.28


Feature selection or variable selection is a data mining scheme for selecting highly relevant features with target concept from high dimensional data. It decreases dimensionality of data, and makes it easy to analyze clusters or classification. A feature selection scheme requires an evaluation function. Most of current evaluation functions are based on statistics or information theory, and they can evaluate only for single feature (one-dimensional data). However, features have interactions between them, and require evaluation function for multi-dimensional data for efficient feature selection. In this study, we propose modification of FSDD evaluation function for utilizing evaluation of multiple features using extended distance function. Original FSDD is just possible for single feature evaluation. Proposed approach may be expected to be applied on other single feature evaluation method.


Grant : Development of a model for optimal growth management of crops in protected horticulture


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