Thesis
An exploration of naïve Bayesian classification augmented with confidence intervals
Washington State University
Master of Science (MS), Washington State University
2010
Handle:
https://hdl.handle.net/2376/102666
Abstract
Instance classification using machine learning techniques has numerous applications, from automation to medical diagnosis. In problem domains such as spam filtering, classification must be performed quickly across large datasets. In this paper we begin with machine learning techniques based on naive Bayes and attempt to improve classification accuracy by taking into account attribute and class confidence intervals. Our classifiers operate over nominal datasets and retain the asymptotic time complexity of linear learning and prediction algorithms. We present results indicating a modest improvement over the naive Bayes classifier alone across a range of multi-class nominal datasets.
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Details
- Title
- An exploration of naïve Bayesian classification augmented with confidence intervals
- Creators
- Paul Anthony Mancill
- Contributors
- Scott Andrew Wallace (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; Pullman, Wash. :
- Identifiers
- 99900525295201842
- Language
- English
- Resource Type
- Thesis