Possibility Rule-Based Classification Using Function Approximation

Abstract

When dealing with real-world problems, uncertainty in information is inevitable. Uncertainty can result from different sources such as noise, ambiguity or lack of knowledge. A category of problems withing the supervised learning framework in which the class membership of the training data is assessed by an expert and expressed in the form of a possibility distribution is considered in this paper. The problem is handled using the possibility theory framework. An accuracy-based learning classifier system designed for function approximation, namely XCSF is employed to model the possibility distribution of different classes. For unseen instances, the predictions from a set of matching rules are fused using an information fusion technique of possibility theory to create an aggregated distribution of possible classes. This distribution may then be either interpreted by a human operator to support decision-making or processed to obtain a final class prediction by selecting the class with the highest possibility. The experimental study with synthetic data reveals the ability of the proposed method to make efficient use of available information in the possibilistic labels of training data compared to conventional classification methods.