In this letter we explore probabilities derived from an import vector machines (IVM) classifier as quantitative measures of class proportion. We have developed a parameter selection strategy that improves the description of class proportions. This strategy incorporates the use of spectral mixtures, which represent gradual class transitions, into the parameter selection process. In addition, we evaluated the sensitivity of our approach in regard to increasing training uncertainty and signal-to-noise ratio. The approach was tested for binary, two-class problems on hyperspectral in situ measurements. The IVM models generated with our parameter selection strategy achieved similar or even improved classification accuracies compared to parameter selection with the standard IVM classification approach. Furthermore, the respective class probabilities correlated highly with reference class proportions. This new strategy is less affected by the inclusion of random noise and relatively stable against increased training errors.