Housing price index forecasting using neural tree model

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@InProceedings{Qi:2009:ISECS-CCCM,

• author = "Feng Qi and Xiyu Liu and Yinghong Ma",
• title = "Housing price index forecasting using neural tree model",
• booktitle = "ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009",
• year = "2009",
• month = aug,
• volume = "2",
• pages = "467--470",
• keywords = "genetic algorithms, genetic programming, Occam razor function, fitness function, housing price index, modified breeder genetic programming, neural tree model, particle swarm optimization, neural nets, particle swarm optimisation, pricing, trees (mathematics)",
• DOI = "doi:10.1109/CCCM.2009.5267470",
• 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 multilayer feedforward network (MLFN) and support vector machine (SVM) models.",
• notes = "Also known as \cite{5267470}",

}

Genetic Programming entries for Feng Qi Xiyu Liu Yinghong Ma

Citations