Abstract
The previous cost-sensitive learning methods assume that the (medical) test cost is measured in the same scale with the misclassification cost while minimizing the expected total cost. This paper proposes a general target-resource framework involving multiple kinds of cost scales, which minimize one kind of cost scale (called target cost scale) through controlling the others (called resource cost scales) in given resource budgets. The proposed cost-sensitive learning model also assists in, such as healthcare data classification and bioinformatics analysis, which are practical and desired application for developing a multiple-scale cost-sensitive learning tool. We experimentally evaluated our approach using the biological and medical datasets, and demonstrated that our proposed method worked well on learning decision tree under a given budget.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.