(2006) Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier. In: ”; the 4th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA-06), March 3-11, 2006, Dubai , UAE.
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Abstract
This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classi- fiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.
| Item Type: | Conference or Workshop Item (Paper) |
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| Uncontrolled Keywords: | Pattern Classification; Functional Networks; Breast cancer detection; Minimum Description Length. |
| Subjects: | Computer |
| Department: | College of Computing and Mathematics > Information and Computer Science |
| Depositing User: | KANAAN ABED FAISAL |
| Date Deposited: | 25 Jun 2008 10:57 |
| Last Modified: | 01 Nov 2019 14:07 |
| URI: | http://eprints.kfupm.edu.sa/id/eprint/14853 |