Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data

Systems-level quality improvement
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Part of the following topical collections:
  1. Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics

Abstract

In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to analyze these samples. The fuzzy expert system offers a clear classification when compared to the machine learning and statistical methodologies. In fuzzy classification, knowledge acquisition would be a major concern. Despite several existing approaches for knowledge acquisition much effort is necessary to enhance the learning process. This paper proposes an innovative Hybrid Stem Cell (HSC) algorithm that utilizes Ant Colony optimization and Stem Cell algorithm for designing fuzzy classification system to extract the informative rules to form the membership functions from the microarray dataset. The HSC algorithm uses a novel Adaptive Stem Cell Optimization (ASCO) to improve the points of membership function and Ant Colony Optimization to produce the near optimum rule set. In order to extract the most informative genes from the large microarray dataset a method called Mutual Information is used. The performance results of the proposed technique evaluated using the five microarray datasets are simulated. These results prove that the proposed Hybrid Stem Cell (HSC) algorithm produces a precise fuzzy system than the existing methodologies.

Keywords

Stem cell optimization Ant colony optimization Fuzzy expert system Mutual information 

References

  1. 1.
    Pomero, F., Di Minno, M. N. D., Fenoglio, L., Gianni, M., Ageno, W., and Dentali, F., Is diabetes a hypercoagulable state? A critical appraisal. Acta Diabetol. 52(6):1007-1016, 2015.Google Scholar
  2. 2.
    Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., and Li, G., An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68:163-172, 2017.Google Scholar
  3. 3.
    Diz, J., Marreiros, G., and Freitas, A., Applying data mining techniques to improve breast cancer diagnosis. J. Med. Syst. 40(9):203, 2016.CrossRefPubMedGoogle Scholar
  4. 4.
    Chang, X., and Yang Y., Semisupervised feature analysis by mining correlations among multiple tasks." IEEE transactions on neural networks and learning systems 28(10):2294-2305, 2017. Google Scholar
  5. 5.
    Wei Liang, Mingdong Tang, Long Jing, Arun Kumar Sangaiah, Yin Huang, (2018) SIRSE: A secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Computers & Electrical Engineering 65:310-321CrossRefGoogle Scholar
  6. 6.
    Zhang, R., Shen J., Wei F., Li X., and Sangaiah A. K., Medical image classification based on multi-scale non-negative sparse coding. Artificial intelligence in medicine 83:44-51, 2017.Google Scholar
  7. 7.
    Yoon, Y., Bien, S., and Park, S., Microarray data classifier consisting of k-top-scoring rank-comparison decision rules with a variable number of genes. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(2):216–226, 2010.CrossRefGoogle Scholar
  8. 8.
    Camara, C., Warwick, K., Bruña, R., Aziz, T., Del Pozo, F., and Maestú, F., A fuzzy inference system for closed-loop deep brain stimulation in Parkinson’s disease. J. Med. Syst. 39(11):155, 2015.CrossRefPubMedGoogle Scholar
  9. 9.
    Vinterbo, S. A., Kim, E. Y., and Ohno-Machado, L., Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics, 21(9):1964-1970, 2005.Google Scholar
  10. 10.
    Wang, Z., and Palade, V., A comprehensive fuzzy-based framework for cancer microarray data gene expression analysis. In BIBE 2007. Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007. (pp. 1003-1010). IEEE, 2007.Google Scholar
  11. 11.
    Chen, S. M., and Tsai, F. M., Generating fuzzy rules from training instances for fuzzy classification systems. Expert Syst. Appl. 35(3):611-621, 2008.Google Scholar
  12. 12.
    Schaefer, G., and Nakashima, T., Data mining of gene expression data by fuzzy and hybrid fuzzy methods. IEEE Trans. Inf. Technol. Biomed. 14(1):23–29, 2010.CrossRefPubMedGoogle Scholar
  13. 13.
    Kumar, P. G., Victoire, T. A. A., Renukadevi, P., and Devaraj, D., Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm. Expert Syst. Appl., 39(2):1811-1821, 2012.Google Scholar
  14. 14.
    Chang, X., Nie, F., Yang, Y., and Huang, H., A convex formulation for semi-supervised multi-label feature selection. In AAAI, pp. 1171-1177, 2014, July.Google Scholar
  15. 15.
    Chen, H. L., Yang, B., Wang, G., Wang, S. J., Liu, J., and Liu, D. Y., Support vector machine based diagnostic system for breast cancer using swarm intelligence. J. Med. Syst. 36(4):2505-2519, 2012.Google Scholar
  16. 16.
    Dorigo, M., and Stutzle, T., Ant Colony Optimization‖. MIT Press, Cambridge, MA, 2004.Google Scholar
  17. 17.
    Lin, K. C., and Hsieh, Y. H., Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. J. Med. Syst. 39(10):119, 2015.Google Scholar
  18. 18.
    Kumar, P. G., Vijay, S. A. A., and Devaraj, D., A hybrid colony fuzzy system for analyzing diabetes microarray data. In computational intelligence in bioinformatics and computational biology (CIBCB), 2013 I.E. Symposium on (pp. 104-111). IEEE, 2013, April.Google Scholar
  19. 19.
    Devaraj, D., Roselyn, J. P., and Rani, R. U., Artificial neural network model for voltage security based contingency ranking. Appl. Soft Comput. 7(3):722-727, 2007.Google Scholar
  20. 20.
    Mak, D. K., A fuzzy probabilistic method for medical diagnosis. J. Med. Syst. 39(3):26, 2015.Google Scholar
  21. 21.
    Pulkkinen, P., and Koivisto, H., Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods. Appl. Soft Comput. 7(2):520–533, 2007.CrossRefGoogle Scholar
  22. 22.
    Samuel, O. W., Zhou, H., Li, X., Wang, H., Zhang, H., Sangaiah, A. K., and Li, G., Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification. Comput. Electr. Eng. 2017.Google Scholar
  23. 23.
    Liao, X., Yin, J., Guo, S., Li, X., and Sangaiah, A. K., Medical JPEG image steganography based on preserving inter-block dependencies. Comput. Electr. Eng. 2017.Google Scholar
  24. 24.
    Dashtban, M., and Balafar, M., Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics 109(2)91-107, 2017.Google Scholar
  25. 25.
    Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., and Nowe, A., A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(4):1106–1119, 2012.CrossRefPubMedGoogle Scholar
  26. 26.
    Wang, J., Wang, H., Zhou, Y., and McDonald, N., Multiple kernel multivariate performance learning using cutting plane algorithm. In 2015 I.E. international conference on Systems, man, and cybernetics (SMC), (pp. 1870-1875). IEEE, 2015, October.Google Scholar
  27. 27.
    Ganesh Kumar, P., and Aruldoss Albert Victoire, T., Multistage mutual information for informative gene selection. J. Biol. Syst. 19(04):725-746, 2011.Google Scholar
  28. 28.
    Taherdangkoo, M., Yazdi, M., and Bagheri, M. H., Stem cells optimization algorithm. In International Conference on Intelligent Computing (pp. 394-403). Springer, Berlin, Heidelberg, 2011, August.Google Scholar
  29. 29.
    Wang, H., and Wang, J., An effective image representation method using kernel classification. In 2014 I.E. 26th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 853-858). IEEE, 2014, November.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKarpagam College of EngineeringCoimbatoreIndia
  2. 2.Department of Information TechnologyAnna University Regional CampusCoimbatoreIndia

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