Development and Comparison of Uncertainty Measures in the Framework of a Data Classification

Abstract

In the analysis and visualization of spatial information, quite often a data classification is applied. The choice of different methods, together with the choice of a different number of classes, the consideration of open classes and the treatment of outliers, can produce very different results. Hence, it is desirable to quantify the uncertainties that inevitably arise in this process. So far, almost only non-spatial properties have been considered. In addition to an extension of this set of statistical measures, this article also aims to define those which are concerned with the preservation of spatial patterns (e.g., local extreme values) as well as with visual perception. An empirical study will investigate the behavior of all these measures, for example depending on the classification method used or the number of classes. Also, correlations between the uncertainty measures and between the measures and statistical properties of the input data are examined. Finally, is will be shown that the uncertainty measures can not only be used individually or combined for pure evaluation purposes, but also for a-posteriori improvement of classification methods.