A Study on Drift Phenomenon of Trained ML

학습된 머신러닝의 표류 현상에 관한 고찰

  • Received : 2022.07.15
  • Accepted : 2022.08.22
  • Published : 2022.08.31

Abstract

In the learned machine learning, the performance of machine learning degrades at the same time as drift occurs in terms of learning models and learning data over time. As a solution to this problem, I would like to propose the concept and evaluation method of ML drift to determine the re-learning period of machine learning. An XAI test and an XAI test of an apple image were performed according to strawberry and clarity. In the case of strawberries, the change in the XAI analysis of ML models according to the clarity value was insignificant, and in the case of XAI of apple image, apples normally classified objects and heat map areas, but in the case of apple flowers and buds, the results were insignificant compared to strawberries and apples. This is expected to be caused by the lack of learning images of apple flowers and buds, and more apple flowers and buds will be studied and tested in the future.

학습된 머신러닝은 시간 경과에 따른 학습 모델과 학습 데이터 측면의 표류 현상이 발생과 동시에 머신러닝의 성능이 퇴화하게 된다. 이를 해결하기 위한 방안으로 머신러닝의 재학습 시기를 결정하기 위한 ML 표류의 개념과 평가 방법을 제안하고자 한다. 딸기와 선명도에 따른 XAI 테스트 및 사과 이미지의 XAI 테스트를 진행하였다. 딸기의 경우 선명도 값에 따른 ML 모델의 XAI 분석의 변화는 미미하였으며 사과 이미지의 XAI의 경우 사과는 정상적으로 객체 분류 및 히트맵 영역을 표시하였으나 사과꽃 및 꽃봉오리의 경우 그 결과가 딸기나 사과에 비해 미미하였다. 이는 사과꽃 및 꽃봉오리의 학습 이미지 수가 부족하기에 발생한 것으로 예상되며 추후 더 많은 사과꽃 및 꽃봉오리 이미지를 학습하여 테스트할 계획이다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2017R1E1A1A03070059). And this work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry(IPET) through (Advanced Production Technology Development Program), funded by ministry of Agriculture, Food and Rural Affairs(MAFRA)(No.320030-3).

References

  1. Andrew P. McMahon, "Machine Learning Engineering with Python - Manage the production life cycle of machine learning models using MLOps with practical examples," Packt Publishing, 2021.
  2. 차윤석, 박진영, 박선, 김종원, 차병래, "ML 모델의 Drift 탐지를 위한 XAI 분석에 따른 머신러닝 모델 팩토리의 제안," 스마트미디어종합학술대회, 2022년 6월
  3. Frank Hutter, Lars Kotthoff, Joaquin Vanschroen, "Automated Machine Learning - Methods, Systems, Challenges," Springer, 2019.
  4. IoU, https://deep-learning-study.tistory.com/402 (accessed Jul. 10, 2022)
  5. 안재현, "XAI 설명 가능한 인공지능, 인공지능을 해부하다," 위키북스, 2020년
  6. NVIDIA KOREA, "설명 가능한 AI란 무엇인가?," https://blogs.nvidia.co.kr/2021/07/27/what-is-explainable-ai/ (accessed Jul. 11, 2022)