A belief function classifier based on information provided by noisy and dependent features

Authors:
Paul-André Monney;Moses Chan;Paul Romberg
Affiliations:
Independent Consultant, 1975 East Pacific Street, Ely, IA 52227, USA;Lockheed Martin Space Systems, Advanced Technology Center, Sunnyvale, CA 94089, USA;Lockheed Martin Space Systems, Advanced Technology Center, Sunnyvale, CA 94089, USA
Venue:
International Journal of Approximate Reasoning
Year:
2011

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Abstract

A model and method are proposed for dealing with noisy and dependent features in classification problems. The knowledge base consists of uncertain logical rules forming a probabilistic argumentation system. Assumption-based reasoning is the inference mechanism that is used to derive information about the correct class of the object. Given a hypothesis regarding the correct class, the system provides a symbolic expression of the arguments for that hypothesis as a logical disjunctive normal form. These arguments turn into degrees of support for the hypothesis when numerical weights are assigned to them, thereby creating a support function on the set of possible classes. Since a support function is a belief function, the pignistic transformation is then applied to the support function and the object is placed into the class with maximal pignistic probability.