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In this research, we study how to generate a decision tree from dataset with unknown values, and proposed a decision tree learning algorithm (LTR-C4.5). The algorithm based on limited tolerance relation and C4.5. Algorithm LTR-C4.5 is composed by two function modules: filling the unknown values and generating a decision tree. The algorithm recursive calls the two function modules when handling incomplete...
This paper proposes a new algorithm for filling the missing values in the incomplete decision table based on extended models of rough sets theory with respect to limited tolerance relation. The algorithm can have the consistence of completed decision table by way of computing the similar degree between objects and limited tolerance relation. It is improvement of ROUSTIDA algorithm, and the experiment...
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