{"title":"Integrating Artificial Neural Network and Taguchi Method on Constructing the Real Estate Appraisal Model","authors":"Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong","volume":93,"journal":"International Journal of Economics and Management Engineering","pagesStart":3010,"pagesEnd":3019,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9999425","abstract":"
In recent years, real estate prediction or valuation has
\r\nbeen a topic of discussion in many developed countries. Improper
\r\nhype created by investors leads to fluctuating prices of real estate,
\r\naffecting many consumers to purchase their own homes. Therefore,
\r\nscholars from various countries have conducted research in real estate
\r\nvaluation and prediction. With the back-propagation neural network
\r\nthat has been popular in recent years and the orthogonal array in the
\r\nTaguchi method, this study aimed to find the optimal parameter
\r\ncombination at different levels of orthogonal array after the system
\r\npresented different parameter combinations, so that the artificial
\r\nneural network obtained the most accurate results. The experimental
\r\nresults also demonstrated that the method presented in the study had a
\r\nbetter result than traditional machine learning. Finally, it also showed
\r\nthat the model proposed in this study had the optimal predictive effect,
\r\nand could significantly reduce the cost of time in simulation operation.
\r\nThe best predictive results could be found with a fewer number of
\r\nexperiments more efficiently. Thus users could predict a real estate
\r\ntransaction price that is not far from the current actual prices.<\/p>\r\n","references":"[1] J.F.C. Khaw, B.S. Lim, and L.E.N. Lim, \"Optimal design of neural\r\nnetworks using the Taguchi method,\u201d Neurocomputing, vol.7, no.3, pp.\r\n225-245, 1995.\r\n[2] A. Tortum, N. Yayla, C. \u00c7elik, and M. G\u00f6kda\u011f, \"The investigation of\r\nmodel selection criteria in artificial neural networks by the Taguchi\r\nmethod,\u201dPhysica A: Statistical Mechanics and its Applications, vol.386,\r\nno.1,pp. 446-468, 2007.\r\n[3] W.C. Chen, Y.Y. Hsu, L.F. Hsieh, and P.H. Tai, \"A systematic\r\noptimization approach for assembly sequence planning using Taguchi\r\nmethod, DOE, and BPNN,\u201d Expert Systems with Applications, vol.37,\r\nno.1, pp. 716-726, 2010.\r\n[4] K.Y. Chang, \"The optimal design for PEMFC modeling based on Taguchi\r\nmethod and genetic algorithm neural networks,\u201d International Journal of\r\nHydrogen Energy, vol.36, no.21, pp. 13683-13694, 2011.\r\n[5] J.R. Jung, and B.J. Yum, \"Artificial neural network based approach for\r\ndynamic parameter design,\u201d Expert Systems with Applications, vol.38,\r\nno.1,pp. 504-510, 2011.\r\n[6] T.B. Asafa, N. Tabet, and S.A.M. Said, \"Taguchi method\u2013ANN\r\nintegration for predictive model of intrinsic stress in hydrogenated\r\namorphous silicon film deposited by plasma enhanced chemical vapour\r\ndeposition,\u201d Neurocomputing, vol. 106, pp. 86-94, 2013.\r\n[7] C.O. Chang. Real estate in the world: trade, investment, agency, policy,\r\nYuan-Liou Publishing, 1990.\r\n[8] W.J. McCluskey, and A.S. Adair, Computer Assisted Mass Appraisal.\r\n1997.\r\n[9] F. Rosenblatt, \"The perceptron - a perceiving and recognizing automaton,\u201d\r\nCornell Aeronautical Laboratory report, pp.85-460-1, 1957.\r\n[10] W.S. McCulloch, and W. Pitts, \"A logical calculus of the ideas immanent\r\nin nervous activity,\u201d The bulletin of mathematical biophysics, vol.5, no.4,\r\npp. 115-133, 1943.\r\n[11] D.O. Hebb, The organization of behavior: a neuropsychological theory.\r\nJohn Wiley and Sons, 1949.\r\n[12] M. Minsky, and S.Papert, Perceptrons. MIT Press. Cambridge, Ma, 1969.\r\n[13] S. Grossberg, \"Adaptive pattern classification and universal recoding: I.\r\nParallel development and coding of neural feature detectors,\u201d Biological\r\nCybernetics, vol.23, no.3,pp. 121-134, 1976.\r\n[14] T. Kohonen, \"Self-organized formation of topologically correct feature\r\nmaps,\u201d Biological Cybernetics, vol.43, no.1,pp. 59-69, 1982.\r\n[15] J.J. Hopfield, \"Neurons with graded response have collective\r\ncomputational properties like those of two-state neurons,\u201d Proceedings of\r\nthe National Academy of Sciences, vol.81, no.10, pp. 3088-3092, 1984.\r\n[16] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, \"Learning\r\nrepresentations by back-propagating errors,\u201d in Neurocomputing:\r\nfoundations of research, A.A. James and R. Edward, Editors. MIT Press.\r\npp. 696-699, 1986.\r\n[17] H.H. Lee, Taguchi Methods: Principles and Practices of Quality Design.\r\nGauLih Book Publishing (4nd), 2013.\r\n[18] Ministry of the Interior of R.O.C., Monthly Bulletin of Interior Statistics,\r\n2014. Available at: http:\/\/sowf.moi.gov.tw\/stat\/month\/list.htm.\r\n[19] J.R. Quinlan, \"Discovering rules by induction from large collections of\r\nexamples,\u201d Expert Systems in the Microelectronic Age, pp. 168-201,\r\n1979.\r\n[20] J.R. Quinlan, \"Induction of decision trees,\u201d Machine Learning, vol. 1,\r\nno.1, pp. 81-106, 1986.\r\n[21] I.C. Yeh, Neural Network Model: Application and Implementation (9rd),\r\nScholars Book Publishing, 2009.\r\n[22] C. Cortes, and V. Vapnik, \"Support-Vector Networks,\u201d Machine\r\nLearning, vol.20, no.3, pp. 273-297, 1995.\r\n[23] S.S. Keerthi, and C.-J. Lin, \"Asymptotic Behaviors of Support Vector\r\nMachines with Gaussian Kernel,\u201d Neural Computation, vol.15, no.7, pp.\r\n1667-1689, 2003.\r\n[24] C.W. Hsu, C.C. Chang, and C.J. Lin, A practical guide to support vector\r\nclassification. 2003.\r\n[25] R.S. Pindyck, and D.L. Rubinfeld, Econometric models and economic\r\nforecasts. Boston, Mass.: Irwin\/McGraw-Hill, 1998.\r\n[26] J. Mingers, \"An empirical comparison of selection measures for\r\ndecision-tree induction,\u201d Machine Learning, 1vol.3, no.4, pp. 319-342,\r\n1989.\r\n[27] U.M. Fayyad, and K.B. Irani, \"On the handling of continuous-valued\r\nattributes in decision tree generation,\u201d Machine Learning, vol.8, no.1, pp.\r\n87-102, 1992.\r\n[28] T.G. Dietterich, \"Ensemble Methods in Machine Learning, in Multiple\r\nClassifier Systems,\u201d Lecture Notes in Computer Science, vol. 1857, pp.\r\n1-15, 2000.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 93, 2014"}