Michigan Style Fuzzy Classification for Gene Expression Analysis

2010 ◽  
pp. 101-108
Author(s):  
Gerald Schaefer ◽  
Tomoharu Nakashima
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Fuzzy Classification

A Combined GA-Fuzzy Classification System for Mining Gene Expression Databases

2010 ◽  
pp. 93-103
Author(s):  
Gerald Schaefer ◽  
Tomoharu Nakashima
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Classification System ◽  
Gene Expression Analysis ◽  
Classification Performance ◽  
Fuzzy Classification ◽  
Good Classification Performance ◽  
Fuzzy Classification System ◽  
Microarray Studies

Microarray studies and gene expression analysis have received significant attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this chapter, the authors show that a combined GA-fuzzy classification system can be employed for effective mining of gene expression data. The applied classifier consists of a set of fuzzy if-then rules that allow for accurate non-linear classification of input patterns. A small number of fuzzy if-then rules are selected through means of a genetic algorithm, and are capable of providing a compact classifier for gene expression analysis. Experimental results on various well-known gene expression datasets confirm good classification performance of our approach.

Sign in / Sign up

Export Citation Format

Share Document