Building cost-sensitive decision trees for medical applications

• Type: Article in International Scientific Journal
• Date: 2011

Authors

Freitas A (Author) FMUP

Journal

Title: AI Communications

Vol. 24

Pages: 285-287

ISSN: 0921-7126

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 13 Citations

Other information

Authenticus ID: P-002-XZ6

DOI: 10.3233/aic-2011-0490

Abstract (EN): This thesis presents strategies for cost-sensitive learning. We have developed an algorithm for decision tree induction that considers various types of costs. The main ones were attribute costs and misclassification costs. Other costs included, for instance, the "risk", that is a measure of how invasive the test is. We applied our strategy to train and to evaluate cost-sensitive decision trees on medical data. The resulting trees provided a better cost-effective solution for a given problem.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3