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Introducing Fluctuation into Increasing Order of Symmetric Uncertainty for Consistency-Based Feature Selection

  • Sho Shimamura
  • Kouichi HirataEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11436)

Abstract

In order to select correlated and relevant features in a feature selection, several filter methods adopt a symmetric uncertainty as one of the feature ranking measures. In this paper, we introduce a fluctuation into the increasing order of the symmetric uncertainty for the consistency-based feature selection algorithms. Here, the fluctuation is an operation of transforming the sorted sequence of features to a new sequence of features. Then, we compare the selected features by the algorithms with a fluctuation with those without fluctuations.

Keywords

Fluctuation Symmetric uncertainty Consistency-based feature selection algorithm Feature selection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of Computer Science and Systems EngineeringKyushu Institute of TechnologyIizukaJapan
  2. 2.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan

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