NDLI: Housing price index forecasting using neural tree model

Content Provider	IEEE Xplore Digital Library
Author	Feng Qi Xiyu Liu Yinghong Ma
Copyright Year	2009
Description	Author affiliation: School of Management and Economics, Shandong Normal University, Jinan, China (Feng Qi; Xiyu Liu; Yinghong Ma)
Abstract	Since the subprime crisis, the variance of housing price is receiving increasing attention especially because of its complexity and practical applications. This paper applies the flexible neural tree model for forecasting the housing price index (HPI). The optimal structure is developed using the Modified Breeder Genetic Programming (MBGP) and the free parameters encoded in the optimal tree are optimized by the Particle Swarm Optimization (PSO), and a new fitness function based on error and Occam's razor is used for for balancing of accuracy and parsimony of evolved structures. Based on the HPI of Shandong province, the performance and efficiency of the applied model are evaluated and compared with the classical multi-layer feed-forward network (MLFN) and support vector machine (SVM) models.
Starting Page	467
Ending Page	470
File Size	304946
Page Count	4
File Format	PDF
ISBN	9781424442478
DOI	10.1109/CCCM.2009.5267470
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Crisis management Occam's razor Fluctuations Communication system control Artificial neural networks modified breeder genetic programming Predictive models Encoding flexible neural tree particle swarm optimization Particle swarm optimization housing price index Genetic programming Cities and towns Economic forecasting
Content Type	Text
Resource Type	Article

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