A Comparative Study on the Performance of Fuzzy Logic, Particle Swarm Optimization, Firefly Algorithm and Cuckoo Search Algorithm Using Residual Analysis

  • Shrayasi DattaEmail author
  • J. Pal Choudhury
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)


Yeast is considered one of the significant elements for medicines and a wide range of chemical products. Various types of Yeast are available. Based on certain initial characteristics (data values) the type of yeast can be ascertained. In this paper, a hybrid model has been proposed to get proper characteristics data values of yeast data so that proper type of yeast data can be ascertained. Here, 50 selected data samples among 1484 samples of yeast dataset have been taken for case study. Here at first Factor analysis (FA) and principal component analysis (PCA) has been applied to find out the cumulative effect of each sample. Then based on Residual error analysis of FA and PCA, cumulative effect value has been taken from FA and thereafter two soft computing models, viz. Particle Swarm Optimization (PSO), Fuzzy Time Series model (FTS) model and two swarm intelligence models viz. firefly algorithm and cuckoo search algorithm have been applied on that cumulative effect data. Finally, using residual analysis their performance has been evaluated.


Fuzzy time series Factor analysis Total effect Classification Particle swarm optimization Cuckoo search algorithm Principal component analysis Firefly algorithm Yeast 


  1. 1.
    Alberts, B.B., Bray, D., Lewis, J., Raff, M., Roberts, K., Watson, J.D.: Molecular Biology of the Cell. Garland, New York (1994)Google Scholar
  2. 2.
    Shavlik, J., Hunter, L., Searls, D.: Introduction. Mach. Learn. 21, 5–10 (1995)Google Scholar
  3. 3.
    Nakai, K., Kanehisa, M.: Expert system for predicting protein localization sites in gram-negative bacteria. Proteins: Struct. Funct. Genet. 11, 95–110 (1991)CrossRefGoogle Scholar
  4. 4.
    Nakai, K., Kanehisa, M.: A knowledge base for predicting protein localization sites in eukaryotic cells. Genomics 14, 897–911 (1992)CrossRefGoogle Scholar
  5. 5.
    Horton, P., Nakai, K.: A probabilistic classification system for predicting the cellular localization sites of proteins. In: Proceedings of Intelligent Systems in Molecular Biology, pp 109–115, St. Louis, USA (1996)Google Scholar
  6. 6.
    Tanwani, A.K., Farooq, M.: Performance Evaluation of evolutionary algorithms in classification of biomedical datasets. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2617–2624 (2009). ACM journal, MontrealGoogle Scholar
  7. 7.
    Mantzaris, D., Anastassopoulos, G., Iliadis, L., Kazakos, K., Papadopoulos, H.: A soft computing approach for osteoporosis risk factor estimation. In: IFIP International Federation for Information Processing, pp. 120–127 (2010)Google Scholar
  8. 8.
    Adam, A.. Shapiai, M.I., Ibrahim, Z., Khalid, M.: Artificial neural network - naïve bayes fusion for solving classification problem of imbalanced dataset. In: 4th IEEE International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Kualalampur, Malaysia, pp. 1–5 (2011)Google Scholar
  9. 9.
    Horton, P., Nakai, K.: Better prediction of protein cellular localization sites with the k nearest neighbor classifier. In: ISMB-97 Proceedings. American Association for Artificial Intelligence, pp. 147–152 (1997)Google Scholar
  10. 10.
    Chen, Y.: Predicting the cellular localization sites of proteins using decision tree and neural networks.
  11. 11.
    Tan, A.C., Gilbert, D.: An empirical comparison of supervised machine learning techniques in bioinformatics. In: Proceedings of the First Asia-Pacific Bioinformatics Conference on Bioinformatics, vol. 19, pp. 419–422. Australian Computer Society, Inc., Australia (2003). ISBN 0-909-92597-6Google Scholar
  12. 12.
    Vorraboot, P., Rasmequan, S., Lursinsap, C., Chinnasarn, K.: A modified error function for imbalanced dataset classification problem. In: 7th IEEE International Conference on Computing and Convergence Technology (ICCCT), Seoul, pp. 854–859 (2012). 978-1-4673-0894-6Google Scholar
  13. 13.
    Ashok, P., Kadhar, G.M., Elayaraja, E., Vadivel, V.: Fuzzy based clustering method on yeast dataset with different fuzzification methods. In: Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–5. IEEE, Tiruchengode, July 2013. ISBN-978-1-4799-3925-1Google Scholar
  14. 14.
    Beheshti, Z., Shamsuddin, S.M.H., Beheshti, E., et al.: Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. J. Soft Comput. 18(11), 2253–2270 (2014). Scholar
  15. 15.
    Thomas, P., Suhner, M.: A new multilayer perceptron pruning algorithm for classification and regression applications. Neural Process. Lett. 42(2), 437–458 (2015). Scholar
  16. 16.
    Datta, S., Palchoudhury, J.: A Comparative study on the performance of fuzzy rule base and artificial neural network towards classification of yeast data. Int. J. Inform. Technol. Comput. Sci. 7(5) (2015)Google Scholar
  17. 17.
    Datta, S., Palchoudhury, J.: A framework for selection of membership function using fuzzy rule base system for the classification of yeast data. In: Proceeding of international conference on Emerging trends in Computer science and Information Technology (ETCSIT 2015), Department of Information Technology, Kalyani Government Engineering College, West Bengal, India, January 2015Google Scholar
  18. 18.
    Datta, S., Palchoudhury, J.: A Framework for selection of neural network training functions towards the classification of yeast data. In: proceeding of National Conference on Computational Technologies-2015, Department of Computer Science and Application, University of North Bengal, India, February 2015Google Scholar
  19. 19.
    Datta, S., Choudhury, J.P.: A framework of multivariant statistical model based tool using particle swarm optimization with fuzzy data for the classification of yeast data. In: 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), pp. 1–7. IEEE, Durgapur (2016)Google Scholar
  20. 20.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy timeseries part I. Fuzzy Sets Syst. 54, 1–9 (1993)CrossRefGoogle Scholar
  21. 21.
    Song, Q., Chissom, B.S.: Fuzzy Time series and its models. Fuzzy Sets and Syst. 54, 269–277 (1993)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy timeseries - part II. Fuzzy Sets Syst. 62, 1–8 (1994)CrossRefGoogle Scholar
  23. 23.
    UCI machine learning repository.

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Authors and Affiliations

  1. 1.Department of Information TechnologyJalpaiguri Government Engineering CollegeJalpaiguriIndia
  2. 2.Futute Institute of Engineering and ManagementSonarpurIndia

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