Building cost-sensitive decision trees for medical applications

Authors:
Alberto Freitas
Affiliations:
CINTESIS - Center for Research in Health Information Systems and Technologies, Portugal
Venue:
AI Communications
Year:
2011

Citing 3
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Abstract

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.