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Computational intelligence based cla...

Chen, Xiujuan.

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Computational intelligence based classifier fusion models for biomedical classification applications.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational intelligence based classifier fusion models for biomedical classification applications./
Author:	Chen, Xiujuan.
Description:	126 p.
Notes:	Advisers: Robert Harrison; Yan-Qing Zhang.
Contained By:	Dissertation Abstracts International68-12B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3293816
ISBN:	9780549380467

Computational intelligence based classifier fusion models for biomedical classification applications.
Chen, Xiujuan.

Computational intelligence based classifier fusion models for biomedical classification applications. - 126 p.

Advisers: Robert Harrison; Yan-Qing Zhang.

Thesis (Ph.D.)--Georgia State University, 2007.

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.

ISBN: 9780549380467Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Computational intelligence based classifier fusion models for biomedical classification applications.
LDR:03315nam 2200313 a 45 001 940715
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020 $a 9780549380467
035 $a (UMI)AAI3293816
035 $a AAI3293816
040 $a UMI $c UMI
100 1 $a Chen, Xiujuan. $3 1264845
245 1 0 $a Computational intelligence based classifier fusion models for biomedical classification applications.
300 $a 126 p.
500 $a Advisers: Robert Harrison; Yan-Qing Zhang.
500 $a Source: Dissertation Abstracts International, Volume: 68-12, Section: B, page: 8121.
502 $a Thesis (Ph.D.)--Georgia State University, 2007.
520 $a 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 $a 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 $a 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 $a School code: 0079.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a Georgia State University. $3 1018518
773 0 $t Dissertation Abstracts International $g 68-12B.
790 $a 0079
790 1 0 $a Harrison, Robert, $e advisor
790 1 0 $a Zhang, Yan-Qing, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3293816