Dimension Reduction of Gene Expression Data for Designing Optimized Rule Base Classifier

  • Amit Paul
  • Jaya Sil
  • Chitrangada Das Mukhopadhyay
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 266)

Abstract

The paper highlights the need of dimension reduction of voluminous gene expression microarray data for developing a robust classifier to predict patients with cancerous genes. The proposed algorithm builds a fuzzy rule based classifier with optimized rule set without much sacrificing classification accuracy. The gene expression matrix is first discretized using linguistic values. The importance factor of each gene is then evaluated representing the degree of presence of a unique linguistic value of the gene both in disease and nondisease classes. Initial fuzzy rule base consists higher ranking genes and gradually other genes are included in the rule base in order to achieve maximum classification accuracy. Thus optimum rule set is built with important genes for classification of test data set. The methodology proposed here has been successfully demonstrated for the lung cancer classification problem, which includes 97 smokers with lung cancer and 90 without lung cancer gene expression data. The results are promising even though maximum number of genes are removed from the original data.

Keywords

Fuzzy rule base Linguistic variable Fuzzy importance 

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Copyright information

© Springer India 2014

Authors and Affiliations

  • Amit Paul
    • 1
  • Jaya Sil
    • 2
  • Chitrangada Das Mukhopadhyay
    • 3
  1. 1.Computer Science and EngineeringSt. Thomas College of Engineering and TechnologyKhidirporeIndia
  2. 2.Computer Science and TechnologyBengal Engineering and Science UniversityShibpurIndia
  3. 3.Health Care Science and TechnologyBengal Engineering and Science UniversityShibpurIndia

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