[1] Pendleton Montague, Brooks King-Casas, and Jonathan Cohen, “Imaging valua- tion models in human choice”, Annual review of neuroscience, 29, pp. 417–48, 02 2006.
[2] Jiafei Niu and Peiqing Niu, “An intelligent automatic valuation system for real estate based on machine learning”, In Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, pp. 1–6, 2019.
[3] Christine Sellar, Jean-Paul Chavas, and John R Stoll, “Specification of the logit model: The case of valuation of nonmarket goods”, Journal of Environmental Economics and Management, 13(4), pp. 382–390, 1986.
[4] Yoney Kirsal Ever, Kamil Dimililer, and Boran Sekeroglu, “Comparison of Ma- chine Learning Techniques for Prediction Problems”, pp. 713–723, 03 2019.
[5] Carson Kai-Sang Leung, Richard Kyle MacKinnon, and Yang Wang, “A Machine Learning Approach for Stock Price Prediction”, In Proceedings of the 18th In- ternational Database Engineering and Applications Symposium, IDEAS ’14, pp. 274–277, New York, NY, USA, 2014, Association for Computing Machinery.
[6] Mahla Nikou, Gholamreza Mansourfar, and J. Bagherzadeh, “Stock price pre- diction using DEEP learning algorithm and its comparison with machine learning algorithms”, Intelligent Systems in Accounting, Finance and Management, 26, 12 2019.
[7] Nor Zulkifley, Shuzlina Rahman, Ubaidullah Nor Hasbiah, and Ismail Ibrahim, “House Price Prediction using a Machine Learning Model: A Survey of Litera- ture”, International Journal of Modern Education and Computer Science, 12, pp. 46–54, 12 2020.
[8] Mohammad H. Rafiei and Hojjat Adeli, “A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units”, Journal of Construction Engineer- ing and Management, 142, pp. 04015066, 08 2015.
[9] Cemil Kuzey, Ali Uyar, and Dursun Delen, “The impact of multinationality on firm value: A comparative analysis of machine learning techniques”, Decision Support Systems, 59, pp. 127–142, 2014.
[10] Xolani Dastile, Turgay Celik, and Moshe Potsane, “Statistical and machine learn- ing models in credit scoring: A systematic literature survey”, Applied Soft Com- puting, 91, pp. 106263, 2020.
[11] Zeljko Kraljevic, Thomas Searle, Anthony Shek, Lukasz Roguski, Kawsar Noor, Daniel Bean, Aurelie Mascio, Leilei Zhu, Amos A. Folarin, Angus Roberts, Re- becca Bendayan, Mark P. Richardson, Robert Stewart, Anoop D. Shah, Wai Keong Wong, Zina Ibrahim, James T. Teo, and Richard J.B. Dobson, “Multi-domain clin- ical natural language processing with MedCAT: The Medical Concept Annotation Toolkit”, Artificial Intelligence in Medicine, 117, pp. 102083, 2021.
[12] Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo, “StarGAN: Unified Generative Adversarial Networks for Multi- Domain Image-to-Image Translation”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
[13] Ela Ertunc, Ahmet Emin Karkinli, and Asli Bozdag, “A clustering-based approach to land valuation in land consolidation projects”, Land Use Policy, pp. 105739, 2021.
[14] Mahamed Omran, Andries Engelbrecht, and Ayed Salman, “An overview of clus- tering methods”, Intell. Data Anal., 11, pp. 583–605, 11 2007.
[15] R. Vidal, Yi Ma, and S. Sastry, “Generalized principal component analysis (GPCA)”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12), pp. 1945–1959, 2005.
[16] Toshihiro Kamishima, “Transfer Learning”, Journal of Japanese Society for Arti- ficial Intelligence, 25(4), pp. 572–580, 2010.
[17] Jeffrey Heaton, “An Empirical Analysis of Feature Engineering for Predictive Modeling”, 01 2017.
[18] Elena Baralis, Silvia Chiusano, and Paolo Garza, “A Lazy Approach to Associative Classification”, IEEE Transactions on Knowledge and Data Engineering, 20(2),pp. 156–171, 2008.
[19] Lyn C. Thomas, “A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers”, International Journal of Forecasting, 16(2), pp. 149–172, 2000.
[20] Sebastian Thrun and Lorien Pratt, “Learning to Learn: Introduction and Overview”, pp. 3–17, Springer US, Boston, MA, 1998.
[21] Yu Zhang and Qiang Yang, “A survey on multi-task learning”, arXiv preprint arXiv:1707.08114, 2017.
[22] Rich Caruana, “Multitask Learning”, Machine Learning, 28, 07 1997.
[23] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y. Ng, “Self-Taught Learning: Transfer Learning from Unlabeled Data”, In Proceedings of the 24th International Conference on Machine Learning, ICML ’07, pp. 759– 766, New York, NY, USA, 2007, Association for Computing Machinery.
[24] Hidetoshi Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function”, Journal of statistical planning and infer- ence, 90(2), pp. 227–244, 2000.
[25] Hal Daume´ and Daniel Marcu, “Domain Adaptation for Statistical Classifiers”, J. Artif. Int. Res., 26(1), pp. 101–126, may 2006.
[26] Bianca Zadrozny, “Learning and Evaluating Classifiers under Sample Selection Bias”, In Proceedings of the Twenty-First International Conference on Machine Learning, ICML ’04, pp. 114, New York, NY, USA, 2004, Association for Com- puting Machinery.
[27] Hirotaka Hachiya, Masashi Sugiyama, and Naonori Ueda, “Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition”, Neurocomputing, 80, pp. 93–101, 2012, Special Issue on Machine Learning for Signal Processing 2010.
[28] Sarkar Das, Mohammed Eunus Ali, Yuan-Fang Li, Yong Bin Kang, and Timos Sel- lis, “Boosting House Price Predictions using Geo-Spatial Network Embedding”, 09 2020.
[29] Byeonghwa Park and Jae Kwon Bae, “Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data”, Ex- pert Systems with Applications, 42(6), pp. 2928–2934, 2015.
[30] Hajime Takada, “How to deal with the depopulation”, Number 95, 2020.
[31] Infrastructure Ministry of Land, “Japanese Real Estate Appraisal Standards”.
[32] Yoji Kiyota, Toshihiko Yamasaki, Hirohiko Suwa, and Chihiro Shimizu, “Real Estate and AI”, The Japanese Society for Artificial Intelligence, 32(4), 2017.
[33] Vladimir Vargas-Calderon and Jorge Camargo, “A model for predicting price po- larity of real estate properties using information of real estate market websites”, 11 2019.
[34] Edwin Lughofer, Bogdan Trawinski, Krzysztof Trawinski, Olgierd Kempa, and Tadeusz Lasota, “On employing fuzzy modeling algorithms for the valuation of residential premises”, Inf. Sci., 181, pp. 5123–5142, 2011.
[35] Tianqi Chen and Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System”, In Proceedings of the 22nd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, KDD ’16, pp. 785–794, New York, NY, USA, 2016, Association for Computing Machinery.
[36] David West, “Neural network credit scoring models”, Computers and Operations Research, 27(11), pp. 1131–1152, 2000.
[37] Ryo Kato, “Inovation on personal loan assessment operations by machine learn- ing”, ESTRELA, (272), 2016.
[38] Zan Huang, Hsinchun Chen, Chia-Jung Hsu, Wun-Hwa Chen, and Soushan Wu, “Credit rating analysis with support vector machines and neural networks: a mar- ket comparative study”, Decision Support Systems, 37(4), pp. 543–558, 2004, Data mining for financial decision making.
[39] Farid Beninel, Waad Bouaguel, and Ghazi Belmufti, “Transfer Learning Using Logistic Regression in Credit Scoring”, 12 2012.
[40] Edward I. Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, The Journal of Finance, 23(4), pp. 589–609, 1968.
[41] Munkhdalai, Lkhagvadorj, Munkhdalai, Tsendsuren, Namsrai, Oyun-Erdene, Lee, Jong Yun, Ryu, and Keun Ho, “An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments”, Sustainability, 11(3), 2019.
[42] Kiyoshi Ono, “Feature of Intec’s credit model and future”, INTEC TECHNICAL JOURNAL, Utilization and application of Big Data, 17, 2016.
[43] Jorge Galindo and Pablo Tamayo, “Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications”, Com- putational Economics, 15, pp. 107–43, 02 2000.
[44] Stjepan Oreski and Goran Oreski, “Genetic algorithm-based heuristic for feature selection in credit risk assessment”, Expert Systems with Applications, 41(4, Part 2), pp. 2052–2064, 2014.
[45] Gang Wang, Jian Ma, Lihua Huang, and Kaiquan Xu, “Two credit scoring models based on dual strategy ensemble trees”, Knowledge-Based Systems, 26, pp. 61– 68, 2012.
[46] Risona Bank, “Present and future of credit assessment method with AI”, Bank of Japan, Workshop for enhancement of finance with AI, 3, 2019.
[47] Hal Daume´ III, “Frustratingly Easy Domain Adaptation”, In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 256– 263, Prague, Czech Republic, June 2007, Association for Computational Linguis- tics.
[48] “FICO Score”.
[49] Dheeru Dua and Casey Graff, “UCI Machine Learning Repository”, 2017.
[50] Home Credit, “Credit scoring data”, Website, 2019.
[51] Yoichi MOTOMURA, Taisuke SATO, Motomura Yoichi, and Sato Taisuke, “Bayesian Networks for Uncertainty Modeling : Uncertain Modeling”, Journal of the Japanese Society for Artificial Intelligence, 15(4), pp. 575–582, jul 2000.
[52] Shota Yunoki, Tomoki Hamagami, Kenji Oshige, Chihiro Kawakami, and Noriyuki Suzuki, “High Accuracy of Call Triage Decision by Bayesian Network”, Electronics and Communications in Japan, 97(1), pp. 62–69, 2014.
[53] Gregory F. Cooper and Edward Herskovits, “A Bayesian method for the induction of probabilistic networks from data”, Machine Learning, 9, pp. 309–347, 2004.
[54] Vladimir Svetnik, Andy Liaw, Christopher Tong, J. Christopher Culberson, Robert P. Sheridan, and Bradley P. Feuston, “Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling”, Journal of Chemical Information and Computer Sciences, 43(6), pp. 1947–1958, 2003, PMID: 14632445.
[55] Scott M. Lundberg and Su-In Lee, “A Unified Approach to Interpreting Model Predictions”, In Proceedings of the 31st International Conference on Neural Infor- mation Processing Systems, NIPS’17, pp. 4768–4777, 2017.
[56] Makoto Shirota, “Information bank and forecast of Credit Scoring business”, NRI media forum, 274, 2019.
[57] Geospatial Information Authority of Japan.
[58] Alexander H.-D. Cheng and Daisy T. Cheng, “Heritage and early history of the boundary element method”, Engineering Analysis with Boundary Elements, 29(3), pp. 268–302, 2005.
[59] Xiaojin Zhu, “Semi-Supervised Learning Literature Survey”, Comput Sci, Uni- versity of Wisconsin-Madison, 2, 07 2008.
[60] Kamal Nigam, Andrew Mccallum, Sebastian Thrun, and Tom Mitchell, “Text Classification from Labeled and Unlabeled Documents using EM”, Machine Learning, 39, pp. 103–134, 05 2000.
[61] Avrim Blum and Tom Mitchell, “Combining Labeled and Unlabeled Data with Co- Training”, In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT’ 98, pp. 92–100, New York, NY, USA, 1998, Association for Computing Machinery.
[62] Thorsten Joachims, “Transductive Inference for Text Classification Using Sup- port Vector Machines”, In Proceedings of the Sixteenth International Conference on Machine Learning, ICML ’99, pp. 200–209, San Francisco, CA, USA, 1999, Morgan Kaufmann Publishers Inc.
[63] Xingquan Zhu and Xindong Wu, “Class Noise Handling for Effective Cost- Sensitive Learning by Cost-Guided Iterative Classification Filtering”, IEEE Trans. on Knowl. and Data Eng., 18(10), pp. 1435–1440, oct 2006.
[64] Qiang Yang, C. Ling, X. Chai, and Rong Pan, “Test-cost sensitive classification on data with missing values”, IEEE Transactions on Knowledge and Data Engi- neering, 18(5), pp. 626–638, 2006.
[65] Dawn E. Holmes and Lakhmi C. Jain, “Introduction to Bayesian Networks”, pp. 1–5, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
[66] Cort J. Willmott and Kenji Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model per- formance”, Climate Research, 30(1), pp. 79–82, 2005.
[67] Akira Namatame, “Cooperative vehicle and network control”, Website, 2009.
[68] Alexander Helleboogh, Giuseppe Vizzari, Adelinde Uhrmacher, and Fabien Michel,“Modeling Dynamic Environments in Multi-Agent Simulation”, Au- tonomous Agents and Multi-Agent Systems, 14, pp. 87–116, 02 2007.