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    A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data

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    Heba's Thesis.pdf
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    Heba's Thesis
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    Type
    Thesis
    Authors
    Abusamra, Heba
    Advisors
    Gao, Xin cc
    Committee members
    Moshkov, Mikhail cc
    Zhang, Xiangliang cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2013-05
    Embargo End Date
    2014-05-11
    Permanent link to this record
    http://hdl.handle.net/10754/292479
    
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    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2014-05-11.
    Abstract
    Microarray technology has enriched the study of gene expression in such a way that scientists are now able to measure the expression levels of thousands of genes in a single experiment. Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification, interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This thesis aims on a comparative study of state-of-the-art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k- nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t- statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used for this study. Different experiments have been applied to compare the performance of the classification methods with and without performing feature selection. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.
    Citation
    Abusamra, H. (2013). A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data. KAUST Research Repository. https://doi.org/10.25781/KAUST-HA0V1
    DOI
    10.25781/KAUST-HA0V1
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-HA0V1
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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