Effect of prior probabilities on the classificatory performance of parametric and mathematical programming approaches to the two-group discriminant problem

Abstract

Abstract. A mixed-integer programming model (MIP) incorporating prior probabilities for the two-group discriminant problem is presented. Its classi catory performance is compared against that of Fisher's linear discrimininant function (LDF) and Smith's quadradic discriminant function (QDF) for simulated data from normal and nonnormal populations for di erent settings of the prior probabilities of group membership. The proposed model is shown to outperform both LDF and QDF for most settings of the prior probabilities when the data are generated from nonnormal populations but underperforms the parametric models for data generated from normal populations.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-β unsaturated aliphatic aldehydes

“…In the former case, the a priori probabilities have to be set to be proportional to the sizes of the groups, while in the latter case the a priori probabilities have to be specified as being equal in each group. The specification of different a priori probabilities affects the accuracy of the prediction [45][46][47][48][49]. Benigni et al [16] assumed prior probabilities of 0.5 for both the mutagenic and non-mutagenicunsaturated aldehydes.…”