Decision trees have been widely used for many years in the statistical literature as powerful, effective, and easily interpretable classification algorithms that are able to automatically select relevant features. This thesis examines in detail both the tree growing phase and the underlying statistical analysis. In addition, various splitting algorithms and stopping rules are explained explicitly to obtain the most powerful tree model. This paper also utilizes numerous techniques to evaluate the accuracy of model. Moreover, the main idea of boosting and its corresponding algorithms (such as AdaBoost, AdaBoost.M1 and AdaBoost.MH) that are used to improve the accuracy of model are also discussed in this paper. Finally, we present two different applications to illustrate how decision tree and boosting method apply to live data.
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 49/05M, Masters Abstracts International|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be