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dc.contributor.advisorKhan, Latifur
dc.creatorHaque, Ahsanul
dc.date.accessioned2018-03-22T20:49:32Z
dc.date.available2018-03-22T20:49:32Z
dc.date.created2017-12
dc.date.issued2017-12
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/10735.1/5664
dc.description.abstractData streams are ubiquitous in today’s digital world. Efficient extraction of knowledge from these streams may help in making important decisions in (near) real-time, and unveiling hidden opportunities. Traditional data mining techniques are inadequate on the streaming data due to its inherent properties, such as infinite length, concept drift, concept evolution, limited labeled data, and covariate shift between labeled training data and unlabeled test data. In this dissertation, we study the challenges posed in data stream classification due to these properties, and propose solutions to the challenges. A data stream is essentially an infinite flow of data. The classifier in a streaming scenario needs to be updated regularly as the underlying class boundary may change and totally new classes may emerge in the stream over time, known as the concept drift and the concept evolution problems respectively. As labeled data instances are scarce in the real-world data streams, the classifier must be trained and updated under a semi-supervised setting in order to capitalize the large portion of data that are unlabeled. Due to the semi-supervised setting, covariate shift such as sampling bias may be introduced between the training and the test distribution. An efficient data stream classification approach would consolidate for this difference in distributions. In addition to addressing these challenges, a classifier in the streaming context must be scalable for addressing the additional challenges posed by any Big Data or Internet of Things (IoT) stream. In this dissertation, we propose four paradigms for addressing challenges in classifying data streams, namely ECHO, FUSION, SDKMM, and CASTLE. First, we propose ECHO, which is a semi-supervised approach for addressing infinite length, concept drift and concept evolution using a limited amount of labeled data. Next, we study the consequences of covariate shift in data stream classification. A covariate shift between the training and test distribution may occur due to difficulty in collecting labeled data instance, often resulting in a sampling bias. In a streaming scenario, we consider two separate streams, where one of the streams provides only labeled training data, and the other stream provides unlabeled test data. These streams of data may have covariate shift and asynchronous concept drifts among them. The second approach proposed in this dissertation, referred to as FUSION, addresses challenges in the above scenario, also known as the Multistream classification problem. In the third and fourth approaches proposed in this dissertation, we propose two scalable paradigms for data stream classification. The third approach, SDKMM, is a sampling-based distributed approach for addressing covariate shift between the training and the test distribution. The last approach presented in this dissertation, referred to as CASTLE, is a hierarchical ensemble classification model for data streams, where individual classifiers in the hierarchy are trained in parallel and in a distributed fashion. We theoretically analyze various properties of the proposed approaches. Moreover, we evaluate each of the above approaches using benchmark datasets, and compare them with a number of baseline approaches. Empirical results indicate the effectiveness of the proposed approaches.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsCopyright ©2017 is held by the author. Digital access to this material is made possible by the Eugene McDermott Library. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectStreaming technology (Telecommunications)
dc.subjectData mining
dc.subjectSupervised learning (Machine learning)
dc.subjectAnalysis of covariance
dc.subjectBig data
dc.subjectInternet of things
dc.titleSemi-supervised Adaptive Classification over Data Streams
dc.typeDissertation
dc.date.updated2018-03-22T20:49:33Z
dc.type.materialtext
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.departmentComputer Science
thesis.degree.levelDoctoral
thesis.degree.namePHD


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