Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions

FieldValue
dc.contributor.authorWang, Xiaoguang
dc.date.accessioned2015-03-27T18:30:17Z
dc.date.available2015-03-27T18:30:17Z
dc.date.created2015
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10393/32184
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-2866
dc.description.abstractWith the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, the Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Existing knowledge discovery and data analyzing techniques have shown great success in many real-world applications such as applying Automatic Target Recognition (ATR) methods to detect targets of interest in imagery, drug activity prediction, computer vision recognition, and so on. Among these techniques, Multiple-Instance (MI) learning is different from standard classification since it uses a set of bags containing many instances as input. The instances in each bag are not labeled | instead the bags themselves are labeled. In this area many researchers have accomplished a lot of work and made a lot of progress. However, there still exist some areas which are not covered. In this thesis, we focus on two topics of MI learning: (1) Investigating the relationship between MI learning and other multiple pattern learning methods, which include multi-view learning, data fusion method and multi-kernel SVM. (2) Dealing with the class imbalance problem of MI learning. In the first topic, three different learning frameworks will be presented for general MI learning. The first uses multiple view approaches to deal with MI problem, the second is a data fusion framework, and the third framework, which is an extension of the first framework, uses multiple-kernel SVM. Experimental results show that the approaches presented work well on solving MI problem. The second topic is concerned with the imbalanced MI problem. Here we investigate the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. For this problem, we propose three solution frameworks: a data re-sampling framework, a cost-sensitive boosting framework and an adaptive instance-weighted boosting SVM (with the name IB_SVM) for MI learning. Experimental results - on both benchmark datasets and application datasets - show that the proposed frameworks are proved to be effective solutions for the imbalanced problem of MI learning.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectMultiple-Instance Learning
dc.subjectBalanced Class Distributions
dc.subjectSkewed Class Distributions
dc.subjectMulti-View
dc.subjectData Fusion
dc.subjectMulti-Kernel SVM
dc.subjectSMOTE
dc.subjectCost-sensitive Boosting
dc.subjectInstance-weighted Boosting SVM
dc.titleDesign and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions
dc.typeThesis
dc.faculty.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
dc.contributor.supervisorJapkowicz, Nathalie
dc.contributor.supervisorMatwin, Stan
dc.degree.namePhD
dc.degree.leveldoctorate
dc.degree.disciplineGénie / Engineering
thesis.degree.namePhD
thesis.degree.levelDoctoral
thesis.degree.disciplineGénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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