A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree Learning

المؤلفون المشاركون

Geibel, Peter
Wysotzki, Fritz

المصدر

Advances in Artificial Intelligence

العدد

المجلد 2009، العدد 2009 (31 ديسمبر/كانون الأول 2009)، ص ص. 1-13، 13ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2009-10-29

دولة النشر

مصر

عدد الصفحات

13

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب
علوم

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-448410