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

I. D. Wilson*, S. E. Kemp, Paul Jarvis

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)136-153
    Number of pages18
    JournalNeural Computing and Applications
    Volume15
    Issue number2
    DOIs
    Publication statusPublished - 1 Apr 2006

    Keywords

    • Artificial neural network
    • Attribute selection
    • C63: computational techniques
    • Gamma test
    • J-score

    Fingerprint

    Dive into the research topics of 'The application of a new attribute selection technique to the forecasting of housing value using dependence modelling'. Together they form a unique fingerprint.

    Cite this