An Initiative Learning Algorithm Based on System Uncertainty

1　Introduction　Initiative learning method is characterized by and henceadvantageous for its independence of prior domain knowl edge. Rough set theory is a good tool for handling uncertaininformation in initiative learning way, because theoreticallyspeaking, data analyses based on rough set model can befully data autonomous and with no necessity of prior or exter nal information1~6]. Possibly thanks to this advantage,rough set theory itself and its application have been kept in…

An Initiative-Learning Algorithm Based on System Uncertainty

Initiative-learning algorithms are characterized by and hence advantageous for their independence of prior domain knowledge.Usually,their induced results could more objectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty,because uncertainty is an intrinsic common feature of and also an essential link between information systems and their induced results.Obviously,the effectiveness of such initiative-learning framework is heavily dependent on the accuracy of system uncertainty measurements.Herein,a more reasonable method for measuring system uncertainty is developed based on rough set theory and the conception of information entropy;then a new algorithm is developed on the bases of the new system uncertainty measurement and the Skowron's algorithm for mining propositional default decision rules.The proposed algorithm is typically initiative-learning.It is well adaptable to system uncertainty.As shown by simulation experiments,its comprehensive performances are much better than those of congeneric algorithms.