Fuzzy system for classification of microarray data using a hybrid ant stem optimisation algorithm

Abstract

Microarray data analysis provides an effective methodology for the diagnosis of diseases and cancers. Although much research has been performed on applying several techniques for microarray data classification during the past years, it has been shown that conventional machine learning and statistical techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents fuzzy based classification system to analyse microarray data. The mutual information approach is used in this approach to extract the most informative genes from the microarray dataset. In the design of fuzzy expert systems, a novel hybrid ant stem (HAS) algorithm used to extract the if-then rules using the membership functions from the given diabetes microarray data is presented. The performance of the proposed technique evaluated using the two diabetes microarray dataset are simulated. These results prove that the proposed HAS algorithm produces a highly accurate fuzzy expert system.