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A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree Learning
Joint Authors
Source
Advances in Artificial Intelligence
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-10-29
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
This article describes how the costs of misclassification given with the individual training objects for classification learning can be used in the construction of decision trees for minimal cost instead of minimal error class decisions.
This is demonstrated by defining modified, cost-dependent probabilities, a new, cost-dependent information measure, and using a cost-sensitive extension of the CAL5 algorithm for learning decision trees.
The cost-dependent information measure ensures the selection of the (local) next best, that is, cost-minimizing, discriminating attribute in the sequential construction of the classification trees.
This is shown to be a cost-dependent generalization of the classical information measure introduced by Shannon, which only depends on classical probabilities.
It is therefore of general importance and extends classic information theory, knowledge processing, and cognitive science, since subjective evaluations of decision alternatives can be included in entropy and the transferred information.
Decision trees can then be viewed as cost-minimizing decoders for class symbols emitted by a source and coded by feature vectors.
Experiments with two artificial datasets and one application example show that this approach is more accurate than a method which uses class dependent costs given by experts a priori.
American Psychological Association (APA)
Wysotzki, Fritz& Geibel, Peter. 2009. A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree Learning. Advances in Artificial Intelligence،Vol. 2009, no. 2009, pp.1-13.
https://search.emarefa.net/detail/BIM-448410
Modern Language Association (MLA)
Wysotzki, Fritz& Geibel, Peter. A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree Learning. Advances in Artificial Intelligence No. 2009 (2009), pp.1-13.
https://search.emarefa.net/detail/BIM-448410
American Medical Association (AMA)
Wysotzki, Fritz& Geibel, Peter. A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree Learning. Advances in Artificial Intelligence. 2009. Vol. 2009, no. 2009, pp.1-13.
https://search.emarefa.net/detail/BIM-448410
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-448410