Value difference metrics for continuously valued attributes

Nearest neighbor and instance-based learning techniques typically handle continuous and linear input values well, but often do not handle symbolic input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between symbolic attribute values, but it largely ignores continuous attributes, using discretization to map continuous values into symbolic values. This paper presents two heterogeneous distance metrics, called the Interpolated VDM (IVDM) and Windowed VDM (WVDM), that extend the Value Difference Metric to handle continuous attributes more appropriately. In experiments on 21 data sets the new distance metrics achieves higher classification accuracy in most cases involving continuous attributes