Comparison of LDA and SPRT on Clinical Dataset Classifications

In this work, we investigate the well-known classification algorithm LDA as well as its close relative SPRT. SPRT affords many theoretical advantages over LDA. It allows specification of desired classification error rates α and β and is expected to be faster in predicting the class label of a new instance. However, SPRT is not as widely used as LDA in the pattern recognition and machine learning community. For this reason, we investigate LDA, SPRT and a modified SPRT (MSPRT) empirically using clinical datasets from Parkinson's disease, colon cancer, and breast cancer. We assume the same normality assumption as LDA and propose variants of the two SPRT algorithms based on the order in which the components of an instance are sampled. Leave-one-out cross-validation is used to assess and compare the performance of the methods. The results indicate that two variants, SPRT-ordered and MSPRT-ordered, are superior to LDA in terms of prediction accuracy. Moreover, on average SPRT-ordered and MSPRT-ordered examine less components than LDA before arriving at a decision. These advantages imply that SPRT-ordered and MSPRT-ordered are the preferred algorithms over LDA when the normality assumption can be justified for a dataset.