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A Model Fusion Based Framework For Imbalanced Classification Problem with Noisy Dataset


Abstract Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data imbalance and data noise have been treated separately in the data mining field. Yet, such approach ignores the mutual effects and as a result may lead to new problems. A desirable solution is to tackle these two issues jointly. Noting the complementary nature of generative and discriminative models, this research proposes a unified model fusion based framework to handle the imbalanced classification wi... (more)
Created Date 2014
Contributor He, Miao (Author) / Wu, Teresa (Advisor) / Li, Jing (Committee member) / Silva, Alvin (Committee member) / Borror, Connie (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / Information science / Gaussian mixture model / Imbalanced classification / K nearest Gaussian / Particle swarm optimization / Support vector machine
Type Doctoral Dissertation
Extent 80 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Industrial Engineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis