지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.5
- 수록면
- 21 - 41 (21page)
- DOI
- 10.7737/JKORMS.2024.49.2.021
이용수
이 논문의 연구 히스토리 (5)
2024
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
This study conducted crawling on data from used car online platforms. As a result, data on 28,751 vehicles including 72 features of vehicle specifications and vehicle options were obtained. And this data were classified by vehicle size. Next, we tried to select the model for the best used car price prediction by vehicle size by executing a ML model. To this purpose, we first execute a Decision Tree based ML models that utilizes the all feature by vehicle size, and feature selection(removing the feature with zero influence) was executed through the Lasso regression model with the same sampling, based on this, a Decision Tree based ML model was re-executed. As a result, it was confirmed that there was no difference in the cost function between them. Next, a stacking ensemble model was executed based on the execution results of the bagging ensemble model and the boosting ensemble model by vehicle size. As a result, the excellence of the stacking ensemble model was not confirmed. Lastly, Tree SHAP Value was visualized for the best models by vehicle size to confirm the attribution level and direction of the feature. Through this, it is intended to prove the usability of the Lasso regression model through feature selection techniques and to support problem solving due to asymmetry of information between used car sales officials.
목차
Abstract
1. 서론
2. 선행연구
3. 자료의 수집 및 특성 선정
4. 머신러닝 모델의 실행 과정
5. 머신러닝 모델의 실행 결과
6. 결론
참고문헌
참고문헌 (0)
참고문헌 신청이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-151-24-02-089904472