As an important classification algorithm,decision trees have been widely used in many fields.Traditional deci-sion tree algorithms,however,do not account for cost constraints in practical applications.Previous research has introduced methods for constructing decision trees under limited cost conditions,but these methods do not consider the time cost asso-ciated with testing samples during the classification process.To minimize the testing time for classifying samples using deci-sion trees,this paper proposes a testing time cost-sensitive decision tree algorithm.It defines the test time cost for samples and introduces a decision index to measure attribute importance.The algorithm for constructing a cost-sensitive decision tree is presented.Experimental results show that the proposed algorithm reduces test-time cost by an average of 11.7％compared to major algorithms such as C4.5,RSDT,and CSGR,while also improving classification accuracy by an average of 5.3％across different datasets.
decision treecost-sensitivedecision indextesting time
万方数据
维普
