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An algorithm for learning from erroneous and incorrigible examples

✍ Scribed by Janusz Kacprzyk; Graz`yna Szkatuła

Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
924 KB
Volume
11
Category
Article
ISSN
0884-8173

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✦ Synopsis

An improved algorithm for inductive learning from erroneous examples is presented. It is assumed that the errors may occur in the attributes' values. However, their location (in which example, and in which attribute) is unknown. Moreover, the errors are assumed incorrigible as it is often the case in practice. A modification of the start-type algorithm is proposed. Importance of the attributes-reflecting, e.g., the attributes' relevance, their proneness to errors, reliability of methods for determining their values, etc.-is elicited from the experts, and weights are determined by Saaty's analytical hierarchy process (AHP). Examples, including an oncological one, illustrating the method proposed are shown.

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