E99-1018 The recursive algorithm for the decision tree induction is shown in Figure 2 . Its parameters
J00-4004 these results to standard CART decision tree induction ( Friedman 1977 ; Breiman et
H89-1053 according to context , we adapted a decision tree induction method based upon ID3 ( Quinlan
H89-1053 selected for splitting . This decision tree induction procedure is used in our application
J00-4004 5.1.4 . The most popular method of decision tree induction , which we employ here , is recursive
J00-4004 for the induction process . 4.2 Decision Tree Induction Another method capable of modeling
J06-2001 tense . Logistic regression , decision tree induction , and genetic programming were
H89-1053 descriptions . The combination of decision tree induction and hierarchical clustering organizes
J00-4004 other domains . However , when decision tree induction was employed to combine only
J00-4004 combined with the correct model . Decision Tree Induction . Decision tree induction successfully
H92-1041 to perform well as a standard decision tree induction method , however , so it is at
J00-4004 individually . For stativity , decision tree induction achieved an accuracy of 93.9
E03-1079 argumenthood and 77.2 % if the decision tree induction is performed exclusively on the
J05-1003 the examples ( for example , the decision tree induction method ) is usually referred
C04-1191 lookup . ( In this case , the decision tree induction process ran into memory constraints
J06-3002 , deverb ) . We use the C5 .0 Decision Tree Induction Algorithm ( Quinlan 1993 ) ,
J00-4004 model . Decision Tree Induction . Decision tree induction successfully combined the 14
J00-4004 accuracies attained by GP and decision tree induction , 68.6 % and 68.5 % respectively
C00-2116 employ the c4 .5 ( Quinhln 1993 ) decision tree induction program as the learning algorithm
J00-4004 classification tasks . For example , decision tree induction used frequency as the main discriminator
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