Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Application Examples Applying Extended Data Expression Technique to Classification Problems

패턴 분류 문제에 확장된 데이터 표현 기법을 적용한 응용 사례

  • Lee, Jong Chan (Deptartment of Computer Engineering, ChungWoon University)
  • 이종찬 (청운대학교 컴퓨터공학과)
  • Received : 2018.10.05
  • Accepted : 2018.12.20
  • Published : 2018.12.28
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Abstract

The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

확장된 데이터 표현의 주요 목표는 유비쿼터스 환경에서 일반적인 문제에 적합한 데이터 구조를 개발하는 것이다. 이 방법의 가장 큰 특징은 속성 값을 확률로 표현할 수 있다는 것이다. 다음 특성은 훈련 데이터의 각 이벤트가 중요도를 나타내는 가중치 값을 갖도록 한다는 것이다. 데이터 구조가 개발된 후에 이를 학습할 수 있는 알고리즘이 고안된다. 그 동안 이 알고리즘은 여러 분야에서 여러 문제에 적용하여 좋은 결과를 산출해 왔다. 본 논문은 먼저 데이터 표현 기법인 UChoo를 소개하고 이론적인 배경이 되는 규칙 개선 문제를 소개한다. 그리고 규칙 개선, 손실 데이터 처리, BEWS 문제, 앙상블 시스템과 같은 응용 분야의 예를 소개한다.

Keywords

OHHGBW_2018_v9n12_9_f0001.png 이미지

Fig. 1. An example of decision tree.

OHHGBW_2018_v9n12_9_f0002.png 이미지

Fig. 2. Rule refinement problem

Table 1. An example of a simple training data set

OHHGBW_2018_v9n12_9_t0001.png 이미지

Table 2. Transform Table 1 into Extended Data Expression.

OHHGBW_2018_v9n12_9_t0002.png 이미지

Table 3. The information derived from the Rules in Fig. 1.

OHHGBW_2018_v9n12_9_t0003.png 이미지

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