지적과 국토정보 (Journal of Cadastre & Land InformatiX)

  • 제46권2호
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  • Pages.43-55
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  • 2016
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  • 2508-3384(pISSN)
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  • 2508-3392(eISSN)
한국국토정보공사 공간정보연구원 (LX Spatial Information Research Institute)
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거리척도와 앙상블 기법을 활용한 지가 추정

Estimating Farmland Prices Using Distance Metrics and an Ensemble Technique

  • 이창로 (서울대학교 국토문제연구소) ;
  • 박기호 (서울대학교 지리학과, 국토문제연구소)
  • Lee, Chang-Ro (Institute for Korean Regional Studies, Seoul National University) ;
  • Park, Key-Ho (Department of Geography, Seoul National University, Institute for Korean Regional Studies)
  • 투고 : 2016.08.23
  • 심사 : 2016.12.07
  • 발행 : 2016.12.10
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초록

본 연구는 사례 기반 학습(instance-based learning)의 논리를 활용하여 지가를 추정하였다. 다양한 사례 기반 학습 기법 중 k-최근린법을 이용하였으며, k-최근린법 적용시 유사성을 측정하는 거리척도는 유클리디안 거리를 비롯해 문헌에 비교적 자주 등장하는 10개의 거리척도를 사용하였다. 본 연구에서는 k-최근린법에 의한 10 종류의 예측값 중 가장 우수한 성능을 보이는 1개의 예측값을 최종 가격으로 선택하는 대신, 이들 예측값들을 병합하는 앙상블(ensemble) 기법의 논리를 적용하여 최종 예측값을 결정하였다. 앙상블 기법 중 일종의 잔차 적합 모형인 경사 부스팅 앨고리듬을 적용하여 최종 가격을 정하였다. 본 연구에서는 이러한 사례 기반 학습과 앙상블 기법의 이점을 실증적으로 제시하기 위해 전라남도 해남군 소재 농지를 사례로 하여 가격을 추정하였으며, k-최근린법에 의한 10 종류의 예측값보다 앙상블 기법에 의한 가격이 보다 정확한 것을 확인할 수 있었다.

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

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