트랜잭션 기반 머신러닝에서 특성 추출 자동화를 위한 딥러닝 응용

• 영문명: A Deep Learning Application for Automated Feature Extraction in Transaction-based Machine Learning
• 발행기관: 한국IT서비스학회
• 저자명: 우덕채(Deock-Chae Woo) 문현실(Hyun Sil Moon) 권순범(Suhnbeom Kwon) 조윤호(Yoonho Cho)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제18권 제2호, 143~159쪽, 전체 17쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2019.06.30

영문 초록

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

목차

1. 서 론

2. 관련 연구

3. 특성 추출 자동화 방법

4. 실험 및 결과

5. 결 론

키워드

Machine Learning Deep Learning Feature Engineering Automated Feature Extraction Transaction Data