Effectiveness of Fuzzy Classifier Rules in Capturing Correlations between Genes

Mohammad Khabbaz, Keivan Kianmehr, Mohammad Alshalalfa and Reda Alhajj
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Mohammad Khabbaz: University of Calgary, Canada
Keivan Kianmehr: University of Calgary, Canada
Mohammad Alshalalfa: University of Calgary, Canada
Reda Alhajj: University of Calgary, Canada and Global University, Lebanon

International Journal of Data Warehousing and Mining (IJDWM), 2008, vol. 4, issue 4, 62-83

Abstract: In this article, we take advantage of using fuzzy classifier rules to capture the correlations between genes. The main motivation to conduct this study is that a fuzzy classifier rule is essentially an “if-then” rule that contains linguistic terms to represent the feature values. This representation of a rule that demonstrates the correlations among the genes is very simple to understand and interpret for domain experts. In our proposed gene selection procedure, instead of measuring the effectiveness of every single gene for building the classifier model, we incorporate the impotence of a gene correlation with other existing genes in the process of gene selection. That is, we reject a gene if it is not in a significant correlation with other genes in the dataset. Furthermore, in order to improve the reliability of our approach, we repeat the process several times in our experiments, and the genes reported as the result are the genes selected in most experiments.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:4:y:2008:i:4:p:62-83

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