TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Machine Learning based Prediction of The Value of Buildings |
| 영문제목(English Title) |
Machine Learning based Prediction of The Value of Buildings |
| 저자(Author) |
Woosik Lee
Namgi Kim
Yoon-Ho Choi
Yong Soo Kim
Byoung-Dai Lee
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| 원문수록처(Citation) |
VOL 12 NO. 08 PP. 3966 ~ 3991 (2018. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings’ value pricing and future value by using big data regarding buildings’ spatial information, as acquired from a database containing building value attributes. The algorithm’s availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings’ value ranking and value pricing, as predicted by intelligent machine learning.
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| 키워드(Keyword) |
Machine Learning
Random Forest
Fully Connected Network
Deep Learning
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| 파일첨부 |
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