An algorithm for learning from erroneous and incorrigible examples

Abstract

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.