머신러닝 기반 의사결정트리를 이용한 서울시 저층주거지의 개별 건축행위 발생구조 분석 = Analysis on Mechanism of Individual Building Development in Low-Rise Residential Area of Seoul using Machine Learning-based Decision Tree
'
머신러닝 기반 의사결정트리를 이용한 서울시 저층주거지의 개별 건축행위 발생구조 분석 = Analysis on Mechanism of Individual Building Development in Low-Rise Residential Area of Seoul using Machine Learning-based Decision Tree' 의 주제별 논문영향력
논문영향력 요약
주제
개별 건축행위
머신 러닝
발생구조
신증축
용도변경
의사 결정 트리
저층 주거지
동일주제 총논문수
논문피인용 총횟수
주제별 논문영향력의 평균
996
0
0.0%
주제별 논문영향력
논문영향력
주제
주제별 논문수
주제별 피인용횟수
주제별 논문영향력
주제어
개별 건축행위
1
0
0.0%
머신 러닝
890
0
0.0%
발생구조
1
0
0.0%
신증축
1
0
0.0%
용도변경
17
0
0.0%
의사 결정 트리
46
0
0.0%
저층 주거지
40
0
0.0%
계
996
0
0.0%
* 다른 주제어 보유 논문에서 피인용된 횟수
0
'
머신러닝 기반 의사결정트리를 이용한 서울시 저층주거지의 개별 건축행위 발생구조 분석 = Analysis on Mechanism of Individual Building Development in Low-Rise Residential Area of Seoul using Machine Learning-based Decision Tree' 의 참고문헌
- Zhang, W., Li, W., Zhang, C., Hanink, D, M., Liu, Y. and Zhai, R., 2018, “Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model”, Landscape and Urban Planning, Vol. 177, pp. 114-127.
- Xu, M., Watanachaturaporn, P., Varshney, P, K. and Arora, M, K., 2005, “Decision tree regression for soft classification of remote sensing data”, Remote Sensing of Environment, Vol.97, No.3, pp. 322-336.
- Westreich, D., Lessler, J. and Funk, M, J., 2010, “Propensity score estimation: neural networks, support vector machines, decision trees(CART), and meta-classifiers as alternatives to logistic regression”, J ournal of Clinical Epidemiology, Vol.63, No.8
- Waddell, P., Wang, L., Charlton, B. and Olsen, A.,., 2010, “Microsimulating parcel-level land use and activity-based travel”, The Journal of Transport and Land Use, Vol.3, No.2, pp. 65-84.
- VnaderPlas, J., 2016, Python data science handbook: essential tools for working with data, O’Reily Media, Inc.
- Venkatasubramaniam, A., Wolfson, J., Mitchell, N., Barnes, T., JaKa, M. and French, S., 2017, “Decision trees in epidemiological research”, Emerging Themes in Epidemiology, Vol.14, No.1, pp. 11.
- Tepe, E. and Guldmann, J. M., 2018, “Spatio-temporal multinomial autologic modeling of land-use change: A parcel-level approach”, Environment and Planning B: Urban analytics and City Science, 2399808318786511
- Sug, H., 2009, “An effect sampling method for decision trees considering comprehensibility and accuracy”, Wseas Transactions on Computers, Vol.8, No.4, pp. 631-640.
- Stevens, D. and Dragicevic, S., 2007, “A GIS-based irregular cellular automata model of land-use change”, Environment and Planning B: Planning and Design, Vol.34, No.4, pp. 708-724.
- Spiekermann, K. and Wegener, M., 2018, “Multi-level urban models: Integration across space, time and policies”, J ournal of Transport and Land Use, Vol. 11, No.1, pp. 67-81.
- Schneider, A., 2012, “Monitoring land cover change in urban and peri-urban areas using sense time stacks of landsat satellite data and a data mining approach”, Remote Sensing of Environment, Vol.123, pp.689-704.
- Samanta, B., Bird, G., Kuijpers, M., Zimmerman, R., Jarvik, G., Wernovsky, G., Clancy, R., Licht, D., Gaynor, J. and Nataraj, C., 2009, “Prediction of periventricular leukomalacia. Part I: Selection of hemodynamic features using logistic regression and
- Naegeli, H. and Sugasawa, K., 2011, “The Xeroderma pigmentosum pathway: Decision tree analysis of DNA quality”, DNA Repair, Vol. 10, pp. 673-683.
- Müller, A. C. and Guido, S., 2016, Introduction to machine learing with Python: a guide for data scientists, O’Reily Media, Inc.
- Moreno, N., Ménard, A. and Marceau, D, J., 2008, “VecGCA: a vector-based geographic cellular automata model allowing geometric transformations of objects”, Environment and Planning B: Planning and Design, Vol.35, No.4, pp. 647-665.
- Moon, S. S., Kang, S, Y., Jitpitaklert, W. and Kim, S, B., 2012, “Decision tree models for characterizing smoking patterns of older adults”, Expert Systems with Applications, Vol.39, No.1, pp. 445-451.
- Menard, A. and Marceau, D, J., 2005, “Exploration of spatial scale sensitivity in geographic cellulalr automata”, Environment and Planning B: Planning and Design, Vol.32, No.5, pp. 693-714.
- Martellozzo, F. and Clarke, K. C., 2011, “Measuring urban sprawl, coalescence, and dispersal: a case study of Pordenone, Italy”, Environment and Planning B: Planning and Design, Vol.28, No.6, pp. 1085-1104.
- Linneman, P., 1980, “Some empirical results on the nature of the hedonic price function for the urban housing market”, Journal of Urban Economics, Vol.8, No.1, pp. 47-68.
- García-Gutiérrez, J., Martínez-Álvarez, F., Troncoso, A. and Riquelme, J. C., 2015, “A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables”, Neurocomputing, Vol.167, pp. 24-31.
- Galligan. D., Ramberg. C., Curtis. C., Ferguson. J. and Fetrow. J., 1990, “Application of portfolio theory In decision tree analysis”, J ournal of Dairy Science, Vol.7, pp. 2138-2144.
- Diappi, L. and Bolchi, P., 2008, “Smith’s rent gap theory and local real estate dynamics: A multi-agent model”, Computers, Environment and Urban Systems, Vol.32, No.1, pp. 6-18.
- Chen, Y. et al., 2017, “Calibrating a Land Parcel Cellular Automaton(LP-CA) for urban growth simulation based on ensemble learning”, International Journal of Geographical Information Science, Vol.31, No.12, pp.2480-2504.
- Chen, M. Y., 2011, “Predicting corporate financial distress based on integration of decision tree classification and logistic regression”, Expert Systems with Applications, Vol.38, No.9, pp. 11261-11272.
- Carrion-Flores, C. and Irwin, E. G., 2004, “Determinants of residential land-use conversion and sprawl at the rural-urban fringe”, American Journal of Agricultural Economics, Vol.86, No.4, pp. 889-904.
- Bhat, C, R., Dubey, S, K., Alam, M, J, B. and Khushefati, W, H., 2015, “A new spatial multiple discrete-continuous modeling approach to land use change analysis”, Journal of Regional Science, Vol.55, No.5, pp. 801-841.
- Batty, M., 2005, “Agents, cells and cities: new representational models for simulating multiscale urban dynamics”, Environment and Planning A, Vol.37, No.8, pp. 1373-1394.
- Abdullahi, S. and Pradhan, B., 2016, “Sustainable brownfields land use change modeling using GIS-based weights-of-evidence approach”, Applied spatial analysis and policy, Vol.9, No.1, pp. 21-38.
"도시특성지표 기반 공간개발 패턴 추 정에 관한 연구”, 한국공간정보학회지, 제23권, 제3호, 한국공 간정보학회
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머신러닝 기반 의사결정트리를 이용한 서울시 저층주거지의 개별 건축행위 발생구조 분석 = Analysis on Mechanism of Individual Building Development in Low-Rise Residential Area of Seoul using Machine Learning-based Decision Tree'
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