Threshold optimization of pseudo-inverse linear discriminants based on overall accuracies
A pseudo-inverse linear discriminants has nothing in common with a Fisher linear discriminant (FLD) if the desired outputs of each sample are changeable. With the customarily desired outputs {1, −1}, a simple and size-related threshold is acquired, which. Multiple thresholds related to sample sizes and distribution regions are thus developed, and the optimal ones may be singled out from among by means of the OCA criterions. Enormous experimental results for the benchmark datasets have verified that the PILDs with optimal thresholds have good learning and generalization performances, and even reach the top OCAs for some datasets among the existing classifiers.