The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Classification of Imbalanced Data Using Multilayer Perceptrons

다층퍼셉트론에 의한 불균현 데이터의 학습 방법

  • 오상훈 (목원대학교 정보통신공학과)
  • Published : 2009.07.28
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Abstract

Recently there have been many research efforts focused on imbalanced data classification problems, since they are pervasive but hard to be solved. Approaches to the imbalanced data problems can be categorized into data level approach using re-sampling, algorithmic level one using cost functions, and ensembles of basic classifiers for performance improvement. As an algorithmic level approach, this paper proposes to use multilayer perceptrons with higher-order error functions. The error functions intensify the training of minority class patterns and weaken the training of majority class patterns. Mammography and thyroid data-sets are used to verify the superiority of the proposed method over the other methods such as mean-squared error, two-phase, and threshold moving methods.

최근에 클래스 분포의 불균형이 심한 데이터의 학습 문제가 그 중요도에 비하여 만족할만한 성능을 얻기 어려운 관계로 관심이 고조되고 있다. 이 문제에 대한 접근 방법은 데이터 레벨의 불균형 해소, 알고리즘 레벨에서의 비용함수 도입, 인식기의 앙상블에 의한 성능향상 등으로 분류된다. 이 논문은 알고리즘 레벨의 접근 방법으로써, 다층퍼셉트론 신경회로망에 고차의 오차함수를 사용하여 불균형 데이터를 학습하는 방법을 제시한다. 즉, 소수클래스의 학습을 강화시키고 다수 클래스의 학습을 약화시키는 형태로 가 중치를 변경시킨다. 클래스 불균형이 심한 유방암 검사와 갑상선 진단 데이터의 학습을 통하여 제안한 방법이 MSE(mean-squaerd error), 2단계 방법 및 문턱조정 방법보다 우수함을 확인한다.

Keywords

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