A framework for optimizing extended belief rule base systems with improved Ball trees

Abstract

Decision support systems imposed a challenge for efficiently reasoning through utilizing stored unordered rules. The Extended Belief Rule-Base (EBRB) system, renowned for its capability of modeling data with vagueness, incompleteness, uncertainty and nonlinear features, requires to traverse over all the rules for the execution of reasoning and consequently suffers reduced inference accuracy and efficiency. To improve the performance, we propose a framework of storing and retrieving belief rules based on employing an optimized structure called improved Ball tree. The framework first constructs a Ball tree index according to the distance between different rules in the metric space, via using the k-means++ algorithm rather than traditionally employed k-centers. Then, through our proposed dynamic threshold radius adjusting method, our algorithm finds an appropriate dataset threshold and accordingly activates more related rules. Moreover, the number of retrieved and activated irrelevant rules is significantly reduced, resulting in consequently improved reasoning accuracy and efficiency. Lastly, three sets of experiments were carried out to validate our algorithm in comparison with the previous EBRB systems.