MARC 主機 00000nam  2200361   4500 
001    AAI3293816 
005    20081125134641.5 
008    081125s2007    ||||||||||||||||| ||eng d 
020    9780549380467 
035    (UMI)AAI3293816 
040    UMI|cUMI 
100 1  Chen, Xiujuan 
245 10 Computational intelligence based classifier fusion models 
       for biomedical classification applications 
300    126 p 
500    Source: Dissertation Abstracts International, Volume: 68-
       12, Section: B, page: 8121 
500    Advisers: Robert Harrison; Yan-Qing Zhang 
502    Thesis (Ph.D.)--Georgia State University, 2007 
520    The generalization abilities of machine learning 
       algorithms often depend on the algorithms' initialization,
       parameter settings, training sets, or feature selections. 
       For instance, SVM classifier performance largely relies on
       whether the selected kernel functions are suitable for 
       real application data. To enhance the performance of 
       individual classifiers, this dissertation proposes 
       classifier fusion models using computational intelligence 
       knowledge to combine different classifiers. The first 
       fusion model called T1FFSVM combines multiple SVM 
       classifiers through constructing a fuzzy logic system. 
       T1FFSVM can be improved by tuning the fuzzy membership 
       functions of linguistic variables using genetic 
       algorithms. The improved model is called GFFSVM. To better
       handle uncertainties existing in fuzzy MFs and in 
       classification data, T1FFSVM can also be improved by 
       applying type-2 fuzzy logic to construct a type-2 fuzzy 
       classifier fusion model (T2FFSVM). T1FFSVM, GFFSVM, and 
       T2FFSVM use accuracy as a classifier performance measure. 
       AUC (the area under an ROC curve) is proved to be a better
       classifier performance metric. As a comparison study, AUC-
       based classifier fusion models are also proposed in the 
       dissertation. The experiments on biomedical datasets 
       demonstrate promising performance of the proposed 
       classifier fusion models comparing with the individual 
       composing classifiers. The proposed classifier fusion 
       models also demonstrate better performance than many 
       existing classifier fusion methods 
520    The dissertation also studies one interesting phenomena in
       biology domain using machine learning and classifier 
       fusion methods. That is, how protein structures and 
       sequences are related each other. The experiments show 
       that protein segments with similar structures also share 
       similar sequences, which add new insights into the 
       existing knowledge on the relation between protein 
       sequences and structures: similar sequences share high 
       structure similarity, but similar structures may not share
       high sequence similarity 
520    INDEX WORDS: Machine Learning, Bioinformatics, DNA 
       Microarray, Protein Structures and Sequences, Classifier 
       Fusion, Computational Intelligence, Support Vector 
       Machines, Fuzzy Logic, Type-2 Fuzzy Logic, Genetic 
       Algorithms, Classifier Performance Measure, Receiver 
       Operating Characteristics 
590    School code: 0079 
590    DDC 
650  4 Biology, Bioinformatics 
650  4 Computer Science 
690    0715 
690    0984 
710 2  Georgia State University 
773 0  |tDissertation Abstracts International|g68-12B 
856 40 |u