Abstract This study focuses on incomplete data classification with the support of different partial discriminant analyses. When samples contain missing values, discriminant analyses such as Principal Component Analysis and Fisher Discriminant Analysis are inapplicable. Partial discriminant analyses that measure the importance of individual dimensions for incomplete data become necessary. Partial discriminant analyses do not rely on data imputation. The analyses can select and sort dimensions (i.e., predictors) based on discriminability in incomplete data. However, the typical approach Fisher Discriminant Ratios may result in biased estimation due to unequal variance of classes according to statistical literature. This study examines various partial discriminant ratios to discover effective approaches for relieving such a problem by considering different variance in computation. Experiments on an open dataset were carried out during the evaluation. Comparisons included discriminability of Partial Fisher Discriminant Ratios, Partial Welch Discriminant Ratios, and their derivatives in incomplete date classification.