ISSN:
1572-9923
Keywords:
bayesian predictive probabilities
;
classification
;
Enterobacteriaceae
;
predictive fit
;
self-organizing artificial intelligence
Source:
Springer Online Journal Archives 1860-2000
Topics:
Biology
Notes:
Abstract 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1010020209899
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