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A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection

Dang, Yue
http://rave.ohiolink.edu/etdc/view?acc_num=wright1502475855457354

Abstract Details

2017, Master of Science in Electrical Engineering (MSEE), Wright State University, Electrical Engineering.
The problem of outlier detection has received increasing attention recently because it plays a great role in many fields such as credit fraud detection, cyber security, etc. Machine Learning approach is an excellent choice for outlier detection due to its accuracy and efficiency. Outlier detection problem is unique due to the so-called classes imbalance: the inliers are extreme majority and the outliers are minority. Ensemble methods are popular in classification and regression task in practice to improve the performance of machine learning algorithms. Bagging and boosting are two common methods of them. In this thesis, we want to show the performance of bagging and boosting compared with base algorithms in outlier detection. First of all, some basic algorithms for outlier detection are described for both supervised and unsupervised methods. Next, theoretical analysis and strategies of ensemble are discussed. Furthermore, groups of experiments are conducted and the experiment results confirm the effectiveness of bagging and boosting methods for outlier detection problem.
Zhiqiang Wu, Ph.D. (Advisor)
Yan Zhuang, Ph.D. (Committee Member)
Bin Wang, Ph.D. (Committee Member)
53 p.
Computer Engineering
computer engineering

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Citations

  • Dang, Y. (2017). A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1502475855457354

    APA Style (7th edition)

  • Dang, Yue. A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection. 2017. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1502475855457354.

    MLA Style (8th edition)

  • Dang, Yue. "A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection." Master's thesis, Wright State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1502475855457354

    Chicago Manual of Style (17th edition)

wright1502475855457354
822
© 2017, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.