인공지능 기반 빈집 추정 및 주요 특성 분석

• 영문명: Vacant House Prediction and Important Features Exploration through Artificial Intelligence: In Case of Gunsan
• 발행기관: 한국IT서비스학회
• 저자명: 임규건(Gyoo Gun Lim) 노종화(Jong Hwa Noh) 이현태(Hyun Tae Lee) 안재익(Jae Ik Ahn)
• 간행물 정보: 『한국IT서비스학회지』제21권 제3호, 63~72쪽, 전체 10쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2022.06.30

영문 초록

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

목차

1. 서 론

2. 이론적 배경

3. 연구방법

4. 결과 및 고찰

5. 결 론

참고문헌

키워드

Vacant house prediction Machine learning XGBoost LightGBM Feature importance