원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 8th International Conference on Next Generation Computing 2022
2022.10
pp.271-273
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Recently, due to the recent significant advances in machine learning and deep learning, it is being utilized in many fields. However, real-world data in the medical field significantly degrades the performance of machine learning algorithms due to problems that are heavily skewed to specific states or that the distribution of data is unbalanced. Therefore, this study solves the problem of not being learned by converting the dependent variable into a regression problem that predicts using a new dependent variable by pseudo labeling. Also, this study present ensemble methods to improve the performance of the model and prevent overfitting.
목차
Abstract
I. INTRODUCTION
II. PROPOSED METHOD
A. Dataset
B. Generating dependent variables with pseudo labeling
III. EXPERMIMENT
A. Experiment result with each model
B. Experiment result with Ensemble model
IV. CONCLUSION
REFERENCES
I. INTRODUCTION
II. PROPOSED METHOD
A. Dataset
B. Generating dependent variables with pseudo labeling
III. EXPERMIMENT
A. Experiment result with each model
B. Experiment result with Ensemble model
IV. CONCLUSION
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보
