Earticle

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

저자

• Wook Lee [ School of Electrical Engineering Korea University Seoul, Korea ]
• Junhee Seok [ School of Electrical Engineering Korea University Seoul, Korea ] Corresponding Author

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