article

Data-Dependent Kernel Machines for Microarray Data Classification

Authors:

Xue-Wen ChenAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 4, Issue 4

Pages 583 - 595

https://doi.org/10.1109/tcbb.2007.1048

Published: 01 October 2007 Publication History

Abstract

One important application of gene expression analysis is to classify tissue samples according to their gene expression levels. Gene expression data are typically characterized by high dimensionality and small sample size, which makes the classification task quite challenging. In this paper, we present a data-dependent kernel for microarray data classification. This kernel function is engineered so that the class separability of the training data is maximized. A bootstrapping-based resampling scheme is introduced to reduce the possible training bias. The effectiveness of this adaptive kernel for microarray data classification is illustrated with a k-Nearest Neighbor (KNN) classifier. Our experimental study shows that the data-dependent kernel leads to a significant improvement in the accuracy of KNN classifiers. Furthermore, this kernel-based KNN scheme has been demonstrated to be competitive to, if not better than, more sophisticated classifiers such as Support Vector Machines (SVMs) and the Uncorrelated Linear Discriminant Analysis (ULDA) for classifying gene expression data.

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Index Terms

Data-Dependent Kernel Machines for Microarray Data Classification
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 4, Issue 4

October 2007

192 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 October 2007

Published in TCBB Volume 4, Issue 4

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Cited By

Ed-Driouch CMoussa A(2019)Improvement of the target selection process in transcriptomics dataProceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society10.1145/3314074.3314090(1-4)Online publication date: 28-Mar-2019
https://dl.acm.org/doi/10.1145/3314074.3314090
Ting KZhu YCarman MZhu YWashio TZhou Z(2019)Lowest probability mass neighbour algorithmsMachine Language10.1007/s10994-018-5737-x108:2(331-376)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10994-018-5737-x
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Zhang AGao X(2018)Supervised data-dependent kernel sparsity preserving projection for image recognitionApplied Intelligence10.1007/s10489-018-1249-448:12(4923-4936)Online publication date: 1-Dec-2018
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Ghorai SMukherjee ASengupta SDutta P(2011)Cancer Classification from Gene Expression Data by NPPC EnsembleIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2010.368:3(659-671)Online publication date: 1-May-2011
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