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Methodology For Generating High-Confidence Cost-Sensitive Rules For Classification

Bakshi, Arjun
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868085

Abstract Details

2013, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Rule based classifiers are often used to make crucial decision in domains like medicine and business intelligence, where there is a need to build insightful models that are quick to train, perform accurate classification, and take the costs of mistakes into account while making or helping with predictions. The existing techniques that address these requirements suffer from some disadvantages that cause them to generate overly complicated rule sets that sometimes do not perform well on new data, or do not take differing misclassification costs into account. The work proposed here aims to build a rule based classifier that extracts rules that have higher support and confidence than existing techniques as well as a classification model that minimizes the cost incurred from misclassifications by making cost sensitive decisions and flagging instances that are likely to be misclassified.
Raj Bhatnagar, Ph.D. (Committee Chair)
Anil Jegga, D.V.M., M.Res. (Committee Member)
Ali Minai, Ph.D. (Committee Member)
67 p.
Computer Science
Cost sensitive; High Confidence; Association Rules; Classification; Non forced

Recommended Citations

Citations

  • Bakshi, A. (2013). Methodology For Generating High-Confidence Cost-Sensitive Rules For Classification [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868085

    APA Style (7th edition)

  • Bakshi, Arjun. Methodology For Generating High-Confidence Cost-Sensitive Rules For Classification. 2013. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868085.

    MLA Style (8th edition)

  • Bakshi, Arjun. "Methodology For Generating High-Confidence Cost-Sensitive Rules For Classification." Master's thesis, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868085

    Chicago Manual of Style (17th edition)

ucin1377868085
421
© 2013, some rights reserved.Creative Commons License Methodology For Generating High-Confidence Cost-Sensitive Rules For Classification by Arjun Bakshi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.