Improved heterogeneous distance functions

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Improved heterogeneous distance functions (1997)

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by D. Randall Wilson , Tony R. Martinez

Venue:	Journal of Artificial Intelligence Research
Citations:	289 - 9 self

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@ARTICLE{Wilson97improvedheterogeneous,
    author = {D. Randall Wilson and Tony R. Martinez},
    title = {Improved heterogeneous distance functions},
    journal = {Journal of Artificial Intelligence Research},
    year = {1997}
}

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Abstract

Instance-based learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.

Keyphrases

continuous attribute heterogeneous distance function nominal attribute value continuous value windowed value difference metric heterogeneous value difference metric new distance metric value difference metric classification accuracy previous distance function interpolated value difference metric nominal attribute nominal value new heterogeneous distance function new distance function nominal input linear input value reasonable distance value

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