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Development of Evaluation Metrics that Consider Data Imbalance between Classes in Facies Classification

지도학습 기반 암상 분류 시 클래스 간 자료 불균형을 고려한 평가지표 개발

  • Kim, Dowan (Dept. of Earth Resources and Environmental Engineering, Hanyang Univ.) ;
  • Choi, Junhwan (Dept. of Earth Resources and Environmental Engineering, Hanyang Univ.) ;
  • Byun, Joongmoo (Dept. of Earth Resources and Environmental Engineering, Hanyang Univ.)
  • 김도완 (한양대학교 자원환경공학과) ;
  • 최준환 (한양대학교 자원환경공학과) ;
  • 변중무 (한양대학교 자원환경공학과)
  • Received : 2020.06.10
  • Accepted : 2020.08.26
  • Published : 2020.08.31

Abstract

In training a classification model using machine learning, the acquisition of training data is a very important stage, because the amount and quality of the training data greatly influence the model performance. However, when the cost of obtaining data is so high that it is difficult to build ideal training data, the number of samples for each class may be acquired very differently, and a serious data-imbalance problem can occur. If such a problem occurs in the training data, all classes are not trained equally, and classes containing relatively few data will have significantly lower recall values. Additionally, the reliability of evaluation indices such as accuracy and precision will be reduced. Therefore, this study sought to overcome the problem of data imbalance in two stages. First, we introduced weighted accuracy and weighted precision as new evaluation indices that can take into account a data-imbalance ratio by modifying conventional measures of accuracy and precision. Next, oversampling was performed to balance weighted precision and recall among classes. We verified the algorithm by applying it to the problem of facies classification. As a result, the imbalance between majority and minority classes was greatly mitigated, and the boundaries between classes could be more clearly identified.

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