KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Dimension Reduction Methods on High Dimensional Streaming Data with Concept Drift

개념 변동 고차원 스트리밍 데이터에 대한 차원 감소 방법

  • 박정희 (충남대학교 컴퓨터공학과)
  • Received : 2016.01.08
  • Accepted : 2016.04.06
  • Published : 2016.08.31
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Abstract

While dimension reduction methods on high dimensional data have been widely studied, research on dimension reduction methods for high dimensional streaming data with concept drift is limited. In this paper, we review incremental dimension reduction methods and propose a method to apply dimension reduction efficiently in order to improve classification performance on high dimensional streaming data with concept drift.

고차원데이터에 대한 차원 감소 기법들은 많이 연구되어져 온 반면, 개념 변동을 가진 고차원 스트리밍 데이터에서 적용할 수 있는 차원 감소 기법에 대한 연구는 제한적이다. 이 논문에서는 스트리밍 데이터에서 적용할 수 있는 점층적 차원 감소 기법들을 살펴보고, 개념 변동 고차원 스트리밍 데이터에 대해 분류 성능을 향상시킬 수 있도록 차원 감소를 효과적으로 적용하는 방법을 제안한다.

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