UNT Theses and Dissertations - 6 Matching Results

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Boosting for Learning From Imbalanced, Multiclass Data Sets

Description: In many real-world applications, it is common to have uneven number of examples among multiple classes. The data imbalance, however, usually complicates the learning process, especially for the minority classes, and results in deteriorated performance. Boosting methods were proposed to handle the imbalance problem. These methods need elongated training time and require diversity among the classifiers of the ensemble to achieve improved performance. Additionally, extending the boosting method to handle multi-class data sets is not straightforward. Examples of applications that suffer from imbalanced multi-class data can be found in face recognition, where tens of classes exist, and in capsule endoscopy, which suffers massive imbalance between the classes. This dissertation introduces RegBoost, a new boosting framework to address the imbalanced, multi-class problems. This method applies a weighted stratified sampling technique and incorporates a regularization term that accommodates multi-class data sets and automatically determines the error bound of each base classifier. The regularization parameter penalizes the classifier when it misclassifies instances that were correctly classified in the previous iteration. The parameter additionally reduces the bias towards majority classes. Experiments are conducted using 12 diverse data sets with moderate to high imbalance ratios. The results demonstrate superior performance of the proposed method compared to several state-of-the-art algorithms for imbalanced, multi-class classification problems. More importantly, the sensitivity improvement of the minority classes using RegBoost is accompanied with the improvement of the overall accuracy for all classes. With unpredictability regularization, a diverse group of classifiers are created and the maximum accuracy improvement reaches above 24%. Using stratified undersampling, RegBoost exhibits the best efficiency. The reduction in computational cost is significant reaching above 50%. As the volume of training data increase, the gain of efficiency with the proposed method becomes more significant.
Date: December 2013
Creator: Abouelenien, Mohamed
Partner: UNT Libraries

Data-Driven Decision-Making Framework for Large-Scale Dynamical Systems under Uncertainty

Description: Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.
Date: August 2016
Creator: Xie, Junfei
Partner: UNT Libraries

A Netcentric Scientific Research Repository

Description: The Internet and networks in general have become essential tools for disseminating in-formation. Search engines have become the predominant means of finding information on the Web and all other data repositories, including local resources. Domain scientists regularly acquire and analyze images generated by equipment such as microscopes and cameras, resulting in complex image files that need to be managed in a convenient manner. This type of integrated environment has been recently termed a netcentric sci-entific research repository. I developed a number of data manipulation tools that allow researchers to manage their information more effectively in a netcentric environment. The specific contributions are: (1) A unique interface for management of data including files and relational databases. A wrapper for relational databases was developed so that the data can be indexed and searched using traditional search engines. This approach allows data in databases to be searched with the same interface as other data. Fur-thermore, this approach makes it easier for scientists to work with their data if they are not familiar with SQL. (2) A Web services based architecture for integrating analysis op-erations into a repository. This technique allows the system to leverage the large num-ber of existing tools by wrapping them with a Web service and registering the service with the repository. Metadata associated with Web services was enhanced to allow this feature to be included. In addition, an improved binary to text encoding scheme was de-veloped to reduce the size overhead for sending large scientific data files via XML mes-sages used in Web services. (3) Integrated image analysis operations with SQL. This technique allows for images to be stored and managed conveniently in a relational da-tabase. SQL supplemented with map algebra operations is used to select and perform operations on sets of images.
Date: December 2006
Creator: Harrington, Brian
Partner: UNT Libraries

Procedural content creation and technologies for 3D graphics applications and games.

Description: The recent transformation of consumer graphics (CG) cards into powerful 3D rendering processors is due in large measure to the success of game developers in delivering mass market entertainment software that feature highly immersive and captivating virtual environments. Despite this success, 3D CG application development is becoming increasingly handicapped by the inability of traditional content creation methods to keep up with the demand for content. The term content is used here to refer to any data operated on by application code that is meant for viewing, including 3D models, textures, animation sequences and maps or other data-intensive descriptions of virtual environments. Traditionally, content has been handcrafted by humans. A serious problem facing the interactive graphics software development community is how to increase the rate at which content can be produced to keep up with the increasingly rapid pace at which software for interactive applications can now be developed. Research addressing this problem centers around procedural content creation systems. By moving away from purely human content creation toward systems in which humans play a substantially less time-intensive but no less creative part in the process, procedural content creation opens new doors. From a qualitative standpoint, these types of systems will not rely less on human intervention but rather more since they will depend heavily on direction from a human in order to synthesize the desired content. This research draws heavily from the entertainment software domain but the research is broadly relevant to 3D graphics applications in general.
Date: May 2005
Creator: Roden, Timothy E.
Partner: UNT Libraries

A Study of Perceptually Tuned, Wavelet Based, Rate Scalable, Image and Video Compression

Description: In this dissertation, first, we have proposed and implemented a new perceptually tuned wavelet based, rate scalable, and color image encoding/decoding system based on the human perceptual model. It is based on state-of-the-art research on embedded wavelet image compression technique, Contrast Sensitivity Function (CSF) for Human Visual System (HVS) and extends this scheme to handle optimal bit allocation among multiple bands, such as Y, Cb, and Cr. Our experimental image codec shows very exciting results in compression performance and visual quality comparing to the new wavelet based international still image compression standard - JPEG 2000. On the other hand, our codec also shows significant better speed performance and comparable visual quality in comparison to the best codec available in rate scalable color image compression - CSPIHT that is based on Set Partition In Hierarchical Tree (SPIHT) and Karhunen-Loeve Transform (KLT). Secondly, a novel wavelet based interframe compression scheme has been developed and put into practice. It is based on the Flexible Block Wavelet Transform (FBWT) that we have developed. FBWT based interframe compression is very efficient in both compression and speed performance. The compression performance of our video codec is compared with H263+. At the same bit rate, our encoder, being comparable to the H263+ scheme, with a slightly lower (Peak Signal Noise Ratio (PSNR) value, produces a more visually pleasing result. This implementation also preserves scalability of wavelet embedded coding technique. Thirdly, the scheme to handle optimal bit allocation among color bands for still imagery has been modified and extended to accommodate the spatial-temporal sensitivity of the HVS model. The bit allocation among color bands based on Kelly's spatio-temporal CSF model is designed to achieve the perceptual optimum for human eyes. A perceptually tuned, wavelet based, rate scalable video encoding/decoding system has been designed and implemented based on this ...
Date: May 2002
Creator: Wei, Ming
Partner: UNT Libraries

Temporally Correct Algorithms for Transaction Concurrency Control in Distributed Databases

Description: Many activities are comprised of temporally dependent events that must be executed in a specific chronological order. Supportive software applications must preserve these temporal dependencies. Whenever the processing of this type of an application includes transactions submitted to a database that is shared with other such applications, the transaction concurrency control mechanisms within the database must also preserve the temporal dependencies. A basis for preserving temporal dependencies is established by using (within the applications and databases) real-time timestamps to identify and order events and transactions. The use of optimistic approaches to transaction concurrency control can be undesirable in such situations, as they allow incorrect results for database read operations. Although the incorrectness is detected prior to transaction committal and the corresponding transaction(s) restarted, the impact on the application or entity that submitted the transaction can be too costly. Three transaction concurrency control algorithms are proposed in this dissertation. These algorithms are based on timestamp ordering, and are designed to preserve temporal dependencies existing among data-dependent transactions. The algorithms produce execution schedules that are equivalent to temporally ordered serial schedules, where the temporal order is established by the transactions' start times. The algorithms provide this equivalence while supporting currency to the extent out-of-order commits and reads. With respect to the stated concern with optimistic approaches, two of the proposed algorithms are risk-free and return to read operations only committed data-item values. Risk with the third algorithm is greatly reduced by its conservative bias. All three algorithms avoid deadlock while providing risk-free or reduced-risk operation. The performance of the algorithms is determined analytically and with experimentation. Experiments are performed using functional database management system models that implement the proposed algorithms and the well-known Conservative Multiversion Timestamp Ordering algorithm.
Date: May 2001
Creator: Tuck, Terry W.
Partner: UNT Libraries