Cultural Algorithm Toolkit for Interactive Knowledge Discovery

Cultural algorithms (CA) are inspired from the cultural evolutionary process in nature and use socialintelligence to solve problems. Cultural algorithms are composed of a belief space which uses differentknowledge sources, a population space and a protocol that enables exchange of knowledge between thesesources. Knowledge created in the population space is accepted into the belief space while this collectiveknowledge from these sources is combined to influence the decisions of the individual agents in solvingproblems. Classification rules comes under descriptive knowledge discovery in data mining and are themost sought out by users since they represent highly comprehensible form of knowledge. The rules havecertain properties which make them useful forms of actionable knowledge to users. The rules are evaluatedusing these properties represented as objective and subjective measures. Objective measures are problemoriented while subjective measures are more user oriented. Evolutionary systems allow the user toincorporate different rule metrics into the solution of a multi objective rule mining problem. However thealgorithms found in the literature allow only certain attributes of the system to be controlled by the user.Research gap exists in providing a complete user controlled system to experiment with evolutionary multiobjective classification rule mining. In the current study a Cultural Algorithm Toolkit for ClassificationRule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters.CAT-CRM allows the user to control the evolutionary parameters, the rule parameters as well as agentparameters and hence can be used for experimenting with an evolutionary system, a rule mining system oran agent based social system. Results of experiments conducted to observe the effect of different crossoverrates and mutation rates on classification accuracy on a bench mark data set is reported.