A Comparative Study on Two Strategies for Distributed Classification

dc.contributor.advisor Wu, Qiang
dc.contributor.author Xu, Honglan
dc.contributor.committeemember Hong, Don
dc.contributor.committeemember Liu, Yeqian
dc.contributor.committeemember Green, Lisa
dc.contributor.department Basic & Applied Sciences en_US
dc.date.accessioned 2018-06-05T20:04:58Z
dc.date.available 2018-06-05T20:04:58Z
dc.date.issued 2018-05-30
dc.description.abstract Distributed learning is an effective tool to process big data. An easy and effective distributed learning approach is the divide and conquer method. It first partitions the whole data set into multiple subsets. A base learning algorithm is then applied to each subset. Finally the results from these subsets are coupled together. In the classification setting, many classification algorithms can be used in the second stage. Typical ones include the logistic regression and support vector machines. For the third stage, both voting and averaging can be used as the coupling strategies. In this thesis, empirical studies are done to thoroughly compare the effectiveness of these two coupling strategies. Averaging is found to be more effective in most scenarios.
dc.description.degree M.S.
dc.identifier.uri http://jewlscholar.mtsu.edu/xmlui/handle/mtsu/5685
dc.publisher Middle Tennessee State University
dc.subject.umi Mathematics
dc.thesis.degreegrantor Middle Tennessee State University
dc.thesis.degreelevel Masters
dc.title A Comparative Study on Two Strategies for Distributed Classification
dc.type Thesis
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