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Novel Extension of k − TSP Algorithm for Microarray Classification

  • Marcin Czajkowski
  • Marek Krętowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

This paper presents a new method, referred as Weight k − TSP, which generates simple and accurate decision rules that can be widely used for classifying gene expression data. The proposed method extends previous approaches: TSP and k − TSP algorithms by considering weight pairwise mRNA comparisons and percentage changes of gene expressions in different classes. Both rankings have been modified as well as decision rules, however the concept of ”relative expression reversals” is retained. New solutions to match analyzed datasets more accurately were also included. Experimental validation was performed on several human microarray datasets and obtained results are promising.

Keywords

Support Vector Machine Microarray Dataset Fuzzy Support Vector Machine Simple Decision Rule Secondary Ranking 
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 2008

Authors and Affiliations

  • Marcin Czajkowski
    • 1
  • Marek Krętowski
    • 1
  1. 1.Faculty of Computer ScienceBiałystok Technical UniversityBiałystokPoland

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