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The application of a new attribute selection technique to the forecasting of housing value using dependence modelling

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Abstract

This article introduces the J-score, a heuristic feature selection technique capable of selecting a useful subset of attributes from a dataset of potential inputs. The utility of the J-score is demonstrated through its application to a dataset containing historical information that may influence the house price index in the United Kingdom. After selecting a subset of features deemed appropriate by the J-score, a predictive model is trained using an artificial neural network. This model is then tested and the results compared with those from an alternative model, built using a subset of features suggested by the Gamma test, a non-linear analysis algorithm that is described. Other control subsets are also used for the assessment of the J-score model quality. The predictive accuracy of the J-score model relative to other models provides evidence that the J-score has good potential for further practical use in a variety of problems in the feature selection domain.

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Author information

Authors and Affiliations

  1. School of Computing, University of Glamorgan, CF37 1DL, Pontypridd , UK

    P. S. Jarvis & I. D. Wilson

  2. School of Technology, University of Glamorgan, CF37 1DL, Pontypridd, UK

    S. E. Kemp

Authors
  1. P. S. Jarvis
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  2. I. D. Wilson
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  3. S. E. Kemp
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Corresponding author

Correspondence to I. D. Wilson.

Appendices

Appendix 1

Graphics of the source data (Figs. 17, 18)

Fig. 17
figure 17

Graphs of the real movement within each economic input series

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Fig. 18
figure 18

Graph showing the real movement of the house price index

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Appendix 2

Graphics of the source data’s annual percentage movement (Figs. 19, 20)

Fig. 19
figure 19

Graphs of the annual percentage movement within each economic input series

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Fig. 20
figure 20

Graph showing the annual percentage movement of the house price index

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Jarvis, P.S., Wilson, I.D. & Kemp, S.E. The application of a new attribute selection technique to the forecasting of housing value using dependence modelling. Neural Comput & Applic 15, 136–153 (2006). https://doi.org/10.1007/s00521-005-0023-9

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  • DOI: https://doi.org/10.1007/s00521-005-0023-9

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