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Homogeneous feature transfer and heterogeneous location fine-tuning for cross-city property appraisal framework
conference contribution
posted on 2019-01-01, 00:00 authored by Y Guo, S Lin, X Ma, J Bal, Chang-Tsun LiChang-Tsun LiMost existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.
History
Event
Australiasian Data Mining. Conference (16th : 2018 : Bathurst, N.S.W.)Volume
996Series
Australiasian Data Mining ConferencePagination
161 - 174Publisher
SpringerLocation
Bathurst, N.S.W.Place of publication
SingaporePublisher DOI
Start date
2018-11-28End date
2018-11-30ISSN
1865-0929ISBN-13
9789811366604Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2019, Springer Nature Singapore Pte Ltd.Editor/Contributor(s)
Rafiqul Islam, Yun Sing Koh, Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Zahidul IslamTitle of proceedings
AusDM 2018 : Proceedings of the 16th Australasian Data Mining Conference 2018Usage metrics
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