Fuzzy Classifier Based Feature Reduction for Better Gene Selection

Khabbaz, Mohammad; Kianmher, Kievan; Alshalalfa, Mohammad; Alhajj, Reda

doi:10.1007/978-3-540-74553-2_31

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the work presented in Khabbaz, et al (2007), we applied our proposal method to a leukemia gene expression dataset, and the achieved results were extremely promising both in terms of the accuracy of the classifier built using genes discovered by our approach and the biological importance of the genes. However, we decided to conduct more experiments on two new datasets and provide comprehensive theoretical and biological analysis to further confirm the validity and effectiveness of our method.…”

Section: Introductionmentioning

confidence: 94%

See 1 more Smart Citation

Effectiveness of Fuzzy Classifier Rules in Capturing Correlations between Genes

Khabbaz

Kianmehr

Alshalalfa

et al. 2008

International Journal of Data Warehousing and Mining

View full text Add to dashboard Cite

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 e�perts. In our proposed gene selection procedure, instead of measuring the effectiveness of every single gene for building the clas� sifier 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 e�periments, and the genes reported as the result are the genes selected in most e�periments.

show abstract

Section: Introductionmentioning

confidence: 94%

“…In this article, we take advantage of using fuzzy classifier rules to capture the correlations between genes; a preliminary version of this study already appeared in Khabbaz, Kiamehr, Alshalalfa, and Alhajj (2007).…”

Section: Introductionmentioning

confidence: 99%