원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 9th International Conference on Next Generation Computing 2023
2023.12
pp.266-268
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Recent deep learning models perform well in image and natural language processing. However, in tabular data, there is a problem that good performance is not achieved due to data-level problems. Recently, TabNet, a model that overcomes these shortcomings, has been widely used for tabular data learning. However, categorical variable data does not perform significantly in tabular data. To solve this problem, Catboost Encoding method is used to solve the problem. In the case of this model, the pre-processing of categorical variable data was well utilized to derive more performance than other models, and it showed better performance than other encoding techniques.
목차
Abstract
I. INTRODUCTION
II. BACKGROUND
A. Categorical Encoding with catboost encoder
B. TabNet
III. PROPOSED METHOD
A. Datasets Description
B. Model Architecture
IV. EXPERMIMENT
A. Experiment result with Tree model
B. Experiment result with deep learining method
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES
I. INTRODUCTION
II. BACKGROUND
A. Categorical Encoding with catboost encoder
B. TabNet
III. PROPOSED METHOD
A. Datasets Description
B. Model Architecture
IV. EXPERMIMENT
A. Experiment result with Tree model
B. Experiment result with deep learining method
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보