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An algorithm for learning from erroneous and incorrigible examples
Author(s) -
Kacprzyk Janusz,
Szkatuła Graz`yna
Publication year - 1996
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199608)11:8<565::aid-int3>3.0.co;2-j
Subject(s) - relevance (law) , reliability (semiconductor) , computer science , analytic hierarchy process , algorithm , process (computing) , artificial intelligence , hierarchy , machine learning , data mining , mathematics , operations research , power (physics) , physics , quantum mechanics , political science , economics , law , market economy , operating system
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. © 1996 John Wiley & Sons, Inc.