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Dissertation
Decision fusion in distributed detection and bioinformatics
Doctor of Philosophy (Ph.D.), Drexel University
Aug 2004
DOI:
https://doi.org/10.17918/etd-336
Abstract
This thesis describes decision fusion architectures and demonstrates decision fusion applications in bioinformatics. In the first part of the thesis, we investigate a new architecture for distributed binary hypothesis detection where all local detectors share a common channel to communicate with the decision fusion center. This architecture is important in the design of sensor fields, where a large number of distributed detectors share a single "emergency" channel. Two window-based algorithms were devised to process the output of the channel and these are analyzed and compared. we then study M-ary hypothesis testing with binary local decisions,a topic of growing interest in the field of distributed detection. We apply genetic algorithms to design local decision rules for such architectures. In the second part of the thesis, we study classification and clustering of large biological datasets. We propose and test a new, fast feature ranking method, and propose a support vector machine (SVM) based classifier fusion scheme. Our methods are demonstrated on surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI TOF MS) datasets, nuclear magnetic resonance (NMR) spectra, and microarray gene expressionprofiles.
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Details
- Title
- Decision fusion in distributed detection and bioinformatics
- Creators
- Yingqin Yuan - DU
- Contributors
- Moshe Kam (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Electrical (and Computer) Engineering [Historical]; Drexel University
- Other Identifier
- 336; 991014632700104721