Integrating multiple rule sets by genetic algorithms

We propose a competition-based knowledge integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each rule in the rule set is first encoded as a rule bit-string. The combined bit strings from multiple rule sets thus form an initial knowledge population. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial rule sets. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is much more accurate than each initial rule set on its own.