Effectiveness of Fuzzy Classifier Rules in Capturing Correlations between Genes

Effectiveness of Fuzzy Classifier Rules in Capturing Correlations between Genes

Mohammad Khabbaz, Keivan Kianmehr, Mohammad Alshalalfa, Reda Alhajj
Copyright: © 2008 |Pages: 22
DOI: 10.4018/jdwm.2008100104
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

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.

Complete Article List

Search this Journal:
Reset
Volume 19: 6 Issues (2023)
Volume 18: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing