이상치 검출 및 그 영향에 관한 연구－ 주거실태조사의 주거환경 만족도평가를 대상으로 －

• 영문명: A Study on the Detection and Impact of Outliers: Focused on the Evaluation of Residential Satisfaction in Housing Survey
• 발행기관: 한국주거환경학회
• 저자명: 최정민(Choi JungMin) 박동찬(Park DongChan)
• 간행물 정보: 『주거환경(한국주거환경학회논문집)』住居環境 제15권 제1호 (통권 제35호), 31~49쪽, 전체 19쪽
• 주제분류: 사회과학 > 지역개발
• 파일형태: PDF
• 발행일자: 2017.02.28

영문 초록

This study analyzed outliers which give a significant impact on reliability in the current Big Data era. We used the Housing Condition Survey (HCS) by the Ministry of Land, Infrastructure and Transport in Korea as a main dataset. We focused on the residential satisfaction of the respondents, detected the outliers, and performed an empirical analysis for impact factors. Although we used the Mahalanobis distance method, which is a common method for outlier detection, we found that this outlier detection method was unreliable. Alternatively, the Naive Bayes Classification method was utilized to detect the outliers and to verify the impact factors. This choice of method was based on the fact that the high correlation among the demographic characteristics and residential satisfaction of respondents are critical elements in the Naive Bayes Classification. The findings include that firstly, about 2,400 samples (12% of total) of the 2014 Housing Condition Survey were detected as outliers. Secondly, it was observed that the tendency of positive over-estimation about questions from the residential satisfaction of respondents in HCS is common. Thirdly, in order to reduce the occurrence of outliers in HCS, it is necessary to lessen the stress of respondents by avoiding long questions in table form.

목차

Abstract

Ⅰ. 서 론

Ⅱ. 이론적 고찰

Ⅲ. 이상치 의심 및 연구모형의 설정

Ⅳ. 연구모형에 의한 실증분석

Ⅴ. 결론

참고문헌

키워드

빅데이터 이상치 베이지안 주거만족도 평가 Big-data Outlier Bayesian Evaluation of Residential Satisfaction