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Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis

  • Shaoning Pang
  • Ilkka Havukkala
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

High dimensionality two-class biomarker data (e.g. microarray and proteomics data with few samples but large numbers of variables) is often difficult to classify. Many currently used methods cannot easily deal with unbalanced datasets (when the number of samples in class 1 and class 2 are very different). This problem can be alleviated by the following new method: first, sample data space by recursive partitions, then use two-class support vector machine tree (2-SVMT) for classification. Recursive partitioning divides the feature space into more manageable portions, from which informative features are more easily found by 2-SVMT. Using two-class microarray and proteomics data for cancer diagnostics, we demonstrate that 2-SVMT results in higher classification accuracy and especially more consistent classification of various datasets than standard SVM, KNN or C4.5. The advantage of the method is its super robustness for class unbalanced datasets.

Keywords

Proteomics Data Recursive Partitioning Unbalanced Dataset Decision Tree Forest Subspace Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shaoning Pang
    • 1
  • Ilkka Havukkala
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering & Discover Research InstituteAuckland University of TechnologyAucklandNew Zealand

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