의사결정나무를 활용한 신경망 모형의 입력특성 선택: 주택가격 추정 사례
Decision Tree-Based Feature-Selective Neural Network Model: Case of House Price Estimation
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.
Abstract
Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.
참고문헌 (15)
- 최정일, "디지털 경제에 부동산 가격의 변동에 영향을 주는요인에 관한 연구", 디지털융복합연구, 11(11), pp.59~70 (2013), 한국디지털정책학회
- 이창로, "단독주택가격 추정을 위한 기계학습 모형의 응용", 대한지리학회지, 51(2), pp.219~233 (2016), 대한지리학회
- 이지영, "인공신경망을 이용한 주택가격지수 예측", 한국산학기술학회논문지, 22(4), pp.228~234 (2021), 한국산학기술학회[DOI]
- Adetunjia, A. B., "House Price Prediction Using Random Forest Machine Learning Technique", Procedia Computer Science, 199, pp.806~813 (2022)
- Kang, J., "Developing A Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence", Sustainability, 12(7), pp.2899~ (2020)
- 강진웅, "의사결정나무와 신경망 모형 결합에 의한 운전자 우회결정요인 분석", 한국도로학회논문집, 13(3), pp.167~176 (2011), 한국도로학회
- 이극노, "이동통신고객 분류를 위한 의사결정나무(C4.5)와 신경망 결합 알고리즘에 관한 연구", 지능정보연구, 9(1), pp.139~155 (2003), 한국지능정보시스템학회
- 안범준, "하이브리드 의사결정나무와 인공신경망 모델을 이용한 방문학습지사의 고객세분화", 한국산학기술학회논문지, 7(3), pp.518~523 (2006), 한국산학기술학회
- 김광섭, "의사결정나무 분류와 인공신경망을 이용한 토양수분 산정모형 개발", 대한토목학회 논문집B, 31(2), pp.155~163 (2011), 대한토목학회
- 최병호, "머신러닝을 이용한 선불전자지급수단의 이상금융거래 탐지 연구", 한국전자거래학회지, 27(2), pp.65~77 (2022), 한국전자거래학회
- McCulloch, W. S., "A Logical Calculus of The Ideas Immanent in Nervous Activity", The Bulletin of Mathematical Biophysics, 5(4), pp.115~133 (1943)
- Adetunjia, A. B., "House Price Prediction Using Random Forest Machine Learning Technique", Procedia Computer Science, 199, pp.806~813 (2022)
- Li, D. K., "Tests for spatial dependence and heterogeneity in spatially autoregressive varying coefficient models with application to Boston house price analysis", Regional Science and Urban Economics, 79, pp.103470~ (2019)
- 정병호, "빅데이터 분류 기법에 따른 벤처 기업의 성장 단계별 차이 분석", (사)디지털산업정보학회 논문지, 15(4), pp.197~212 (2019), (사)디지털산업정보학회
- 윤한성, "속성유사도에 따른 사회연결망 서브그룹의 군집유효성", (사)디지털산업정보학회 논문지, 17(1), pp.75~84 (2021), (사)디지털산업정보학회