The aim of this study is to explore whether the number of intervals for each attribute influences the classification result and whether a larger number of intervals provide better classification accuracy using the Fuzzy PRISM algorithm. The feature selection has been carried out using Fast correlation-based filter solution, and then the decreased data sets have been applied in experiments with preferences used in the previous experiment series. The article also provides conclusions about the obtained classification results and analyzes criteria of certain experiments and their impact on the final result. Also a series of experiments was carried out to assess how and whether the classification result is influenced by categorization of continuous data, which is one of the membership function construction steps; Fuzzy unordered rule induction algorithm was used. The experiments have been carried out using four real data sets – Golub leukemia, Singh prostate, as well as Gastric cancer and leukemia donor data sets of the Latvian Biomedical Research and Study Center.