Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Classifier Capable of Handling Incomplete Data Set

불완전한 데이터를 처리할수 있는 분류기

  • 이종찬 (청운대학교 인터넷학과) ;
  • 이원돈 (충남대학교 전기정보통신공학부 컴퓨터)
  • Published : 2010.01.30
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Abstract

This paper introduces a classification algorithm which can be applied to a learning problem with incomplete data sets, missing variable values or a class value. This algorithm uses a data expansion method which utilizes weighted values and probability techniques. It operates by extending a classifier which are considered to be in the optimal projection plane based on Fisher's formula. To do this, some equations are derived from the procedure to be applied to the data expansion. To evaluate the performance of the proposed algorithm, results of different measurements are iteratively compared by choosing one variable in the data set and then modifying the rate of missing and non-missing values in this selected variable. And objective evaluation of data sets can be achieved by comparing, the result of a data set with non-missing variable with that of C4.5 which is a known knowledge acquisition tool in machine learning.

본 논문은 변수 값들이나 부류 값을 손실한, 불완전한 데이터를 포함하는 데이터 집합을 가지고 학습하는 문제에 적용될 수 있는 분류 알고리즘을 소개한다. 이 알고리즘은 가중치 값과 확률 기법들을 이용하는 데이터 확장 방법을 사용한다. 이는 휘셔(Fisher)의 식을 기반으로 최적의 투사 면이 되도록 고려된 분류기를 확장함으로써 수행한다. 이를 위해, 데이터 확장에 적용되는 과정으로 부터 몇몇 식들이 유도된다. 제안한 알고리즘의 성능평가를 위해, 데이터에서 하나의 변수를 선택하고 이 선택된 변수에 소실 값과 소실되지 않은 값들의 비율을 변형함에 의해 다른 측정값들의 결과들이 반복적으로 비교된다. 또한 데이터 집합의 객관적인 평가를 위해 기계학습에서 지식 습득 도구로 널리 쓰이는 C4.5의 결과와 비교한다.

Keywords

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