Abstract: Following the derivation of multiple reducts,an ensemble based classification framework can be constructed,which has been demonstrated to be useful in improving the performance of subsequent learning tasks.The approach called super-reduct is exactly suggested with such thinking.Generally,multiple super-reducts are obtained by randomly adding more extra attributes into the fundamental reduct.Therefore,how to search fundamental reduct is the key to performing super-reduct.In view of this,considering both efficiency and effectiveness,not only attribute group but also ensemble selector is introduced into the mechanism of super-reduct:the device of attribute group is used to speed up the process of searching fundamental reduct,the device of ensemble selector is used to find more robust attributes in the procedure of searching reduct.Comprehensive experiments on 20 UCI data sets show that compared with 4 popular strategies,our approach can not only significantly reduce the computational cost but also provide superior stabilities and accuracies for classification tasks.

Key words: Attribute group, Ensemble selector, Searching reduct, Super-reduct