The management of uncertainty has received much attention recently in the fields of database and artificial intelligence. Several methods of evidential reasoning have been proposed for real‐world problems with which uncertainty is associated. Considers one of these problems, that of classification, which is encountered in many domains including medicine. Focuses on a classification technique for knowledge discovery (KD). Reasoning about classifications is a primary interest in KD. Deals with obtaining evidence to confirm or refute classes. Searches for any data dependencies which exist between a classifier attribute and any of the property attributes. To illustrate the method compares a neural network classification with one based on Tanimoto’s method. It is important to note that the aim is to demonstrate this approach rather than to compare these two methods of classification. After extracting the data dependency information, employs a non‐numeric evidential reasoning method to see how well this evidence supports each of the two respective classifications.
McErlean, F., Bell, D.A., Barr, A. and Mulvenna, G. (1997), "Classification of knee problems to provide evidential support", Kybernetes, Vol. 26 No. 5, pp. 574-588. https://doi.org/10.1108/03684929710176539Download as .RIS
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