An Improved Microarray Gene Expression Classification Using Fuzzy Expert System and Ant Bee Algorithm

Authors

  • S. Deepakkumar  Computer Science and Engineering, Sri Vidya College of Engineering and Technology, Virudhunagar, Tamilnadu, India
  • M. Mohankumar  Computer Science and Engineering, Sri Vidya College of Engineering and Technology, Virudhunagar, Tamilnadu, India
  • Dr. P. Murugeswari  Computer Science and Engineering, Sri Vidya College of Engineering and Technology, Virudhunagar, Tamilnadu, India

Keywords:

Medical Diagnosis, Fuzzy expert system, Micro array data, artificial bee colony, ant colony optimization and mutual information.

Abstract

Medical diagnosis and dealing of micro array data can be done effectively by means of fuzzy expert system. The main intention of using fuzzy system is high perfection and minimum complexity in dealing with classification of medical data. The existing GSA (Genetic Swarm Algorithm) enables high precision in classification on fuzzy expert system with desired cost on medical research. The main drawback of GSA is if-then rules which is multifaceted and protracted that’s highly complicated, those are difficult for a physician to understand. In order to address the interpretability-accuracy tradeoff, a development is made in presenting the rule set by the combination of integer numbers and the task of rule generation. Ant colony optimization (ACO) generates simple rule set according to the gene expression values by which fuzzy partition is applied. But it still suffers from addressing the formless and continuous expression values of a gene. In this paper, we propose artificial bee colony (ABC) algorithm based on mutual information. This mutual information effective in analyzing the informative genes and the improved proposed mechanism hybrid Ant Bee Algorithm (ABA) using fuzzy-II logic is computed with six gene expression data sets in order to examine its effectiveness. The results shows our improved proposed mechanism achieves more perfection in fuzzy system by the combination of highly interpretable and compact rules among all the data sets thus proves its performance is far better than other traditional mechanisms.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2016-06-30

Issue

Volume 2, Issue 3, May-June-2016

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Research Articles

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How to Cite

[1]
S. Deepakkumar, M. Mohankumar, Dr. P. Murugeswari, " An Improved Microarray Gene Expression Classification Using Fuzzy Expert System and Ant Bee Algorithm, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.722-727, May-June-2016.