An Assessment of Cumulative Classification

We present a method for building systematics when new knowledge is continuously accumulated. The resulting classification is self-correcting and improves itself by sorting new items as they are added to the material and studied. The formulation is based on Bayesian predictive probability distributions. A new item that has not yet been classified is assigned to the class that has maximal posterior probability or is made to form a group of its own. Such a cumulative classification depends on the order in which the items are classified. The introduction of an already classified training set considerably improves the repeatability of the method. As a case study we applied the method to a large data set for the Enterobacteriaceae. The resulting classifications corresponded well to the general structure of the prevailing taxonomy of Enterobacteriaceae.