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