Probabilistic logic reasoning for subjective interestingness analysis

Abstract

This paper presents an approach that uses probabilistic logic reasoning to compute subjective interestingness scores for classification rules. In the proposed approach, domain knowledge is represented as a probabilistic logic program that encodes information from experts and statistical reports. The computation of interestingness scores is performed by a procedure that applies linear programming to reasoning regarding the probabilities of interest. It provides a mechanism to calculate probability-based subjective interestingness scores. Further, a sample application illustrates the use of the described approach.