Title of article :
IMPROVING THE ACCURACY OF DECISION TREE INDUCTION BY FEATURE PRESELECTION
Author/Authors :
Perner، Petra نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2001
Pages :
-746
From page :
747
To page :
0
Abstract :
Selecting the right set of features for classification is one of the most important problems in designing a good classifier. Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy, while some other statistical classification algorithms require the feature subset to be selected in a preprocessing phase. It is well known that correlated and irrelevant features may degrade the performance of the C4.5 algorithm. In our study, we evaluated the influence of feature preselection on the prediction accuracy of C4.5 using a real-world data set. We observed that accuracy of the C4.5 classifier could be improved with an appropriate feature preselection phase for the learning algorithm. Beyond that, the number of features used for classification can be reduced, which is important for image interpretation tasks since feature calculation is a time-consuming process.
Journal title :
Applied Artificial Intelligence
Serial Year :
2001
Journal title :
Applied Artificial Intelligence
Record number :
52006
Link To Document :
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