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Volume 13 Number 1 >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/720

Title: Using Sensitivity as a Method for Ranking the Test Cases Classified by Binary Decision Trees
Authors: Noblesse, Sabrina
Vanhoof, Koen
Keywords: Learning Induction
Concept Learning
Classifier Design and Evaluation
Issue Date: 2006
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: Usually, data mining projects that are based on decision trees for classifying test cases will use the probabilities provided by these decision trees for ranking classified test cases. We have a need for a better method for ranking test cases that have already been classified by a binary decision tree because these probabilities are not always accurate and reliable enough. A reason for this is that the probability estimates computed by existing decision tree algorithms are always the same for all the different cases in a particular leaf of the decision tree. This is only one reason why the probability estimates given by decision tree algorithms can not be used as an accurate means of deciding if a test case has been correctly classified. Isabelle Alvarez has proposed a new method that could be used to rank the test cases that were classified by a binary decision tree [Alvarez, 2004]. In this paper we will give the results of a comparison of different ranking methods that are based on the probability estimate, the sensitivity of a particular case or both.
URI: http://hdl.handle.net/10525/720
ISSN: 1313-0463
Appears in Collections:Volume 13 Number 1

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