Medical & Biological Engineering & Computing

, Volume 57, Issue 12, pp 2673–2682 | Cite as

Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function

  • Kaveri Chatra
  • Venkatanareshbabu KuppiliEmail author
  • Damodar Reddy Edla
  • Ajeet Kumar Verma
Original Article


Cancer classification is one of the crucial tasks in medical field. The gene expression of cells helps in identifying the cancer. The high dimensionality of gene expression data hinders the classification performance of any machine learning models. Therefore, we propose, in this paper a methodology to classify cancer using gene expression data. We employ a bio-inspired algorithm called binary bat algorithm for feature selection and extreme learning machine for classification purpose. We also propose a novel fitness function for optimizing the feature selection process by binary bat algorithm. Our proposed methodology has been compared with original fitness function that has been found in the literature. The experiments conducted show that the former outperforms the latter.

Graphical Abstract

Classification using Binary Bat Optimization and Extreme Learning Machine


Gene Cancer DNA 



  1. 1.
    Abdullah AS, Ramya C, Priyadharsini V, Reshma C, Selvakumar S (2017) A survey on evolutionary techniques for feature selection. In: 2017 conference on emerging devices and smart systems (ICEDSS). IEEE, pp 58–62Google Scholar
  2. 2.
    Andaru W, Syarif I, Barakbah AR (2017) Feature selection software development using artificial bee colony on dna microarray data. In: 2017 international electronics symposium on knowledge creation and intelligent computing (IES-KCIC). IEEE, pp 6– 11Google Scholar
  3. 3.
    Banka H, Dara S (2012) Feature selection and classification for gene expression data using evolutionary computation. In: 2012 23rd international workshop on database and expert systems applications (DEXA). IEEE, pp 185–189Google Scholar
  4. 4.
    Chao S, Lihui C (2004) High dimensional gene expression data dimension reduction. In: 2004 IEEE conference on cybernetics and intelligent systems. IEEE, vol 1, pp 451–455Google Scholar
  5. 5.
    Chuang LY, Chang HW, Tu CJ, Yang CH (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38CrossRefGoogle Scholar
  6. 6.
    Dara S, Banka H (2014) A binary PSO feature selection algorithm for gene expression data. In: 2014 international conference on advances in communication and computing technologies (ICACACT). IEEE, pp 1–6Google Scholar
  7. 7.
    Dougherty ER (2001) Small sample issues for microarray-based classification. Compar Funct Genom 2(1):28–34CrossRefGoogle Scholar
  8. 8.
    Fahrudin TM, Syarif I, Barakbah AR (2016) Ant colony algorithm for feature selection on microarray datasets. In: 2016 international electronics symposium (IES). IEEE, pp 351–356Google Scholar
  9. 9.
    Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537CrossRefGoogle Scholar
  10. 10.
    Hasnat A, Molla AU (2016) Feature selection in cancer microarray data using multi-objective genetic algorithm combined with correlation coefficient. In: International conference on emerging technological trends (ICETT). IEEE, pp 1–6Google Scholar
  11. 11.
    Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004. proceedings. 2004 IEEE international joint conference on neural networks. IEEE, vol 2, pp 985–990Google Scholar
  12. 12.
    Lazar C, Taminau J, Meganck S, Steenhoff D, Coletta A, Molter C, de Schaetzen V, Duque R, Bersini H, Nowe A (2012) A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 9(4):1106–1119CrossRefGoogle Scholar
  13. 13.
    Liang Y, Liu C, Luan XZ, Leung KS, Chan TM, Xu ZB, Zhang H (2013) Sparse logistic regression with a l 1/2 penalty for gene selection in cancer classification. BMC Bioinform 14(1):198CrossRefGoogle Scholar
  14. 14.
    Lorena AC, Costa IG, de Souto MC (2008) On the complexity of gene expression classification data sets. In: 2008. HIS’08. Eighth international conference on hybrid intelligent systems. IEEE, pp 825–830Google Scholar
  15. 15.
    Lu Y, Han J (2003) Cancer classification using gene expression data. Inf Syst 28(4):243–268CrossRefGoogle Scholar
  16. 16.
    Lv J, Peng Q, Chen X, Sun Z (2016) A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst Appl 59:13–19CrossRefGoogle Scholar
  17. 17.
    Pavithra D, Lakshmanan B (2017) Feature selection and classification in gene expression cancer data. In: 2017 international conference on computational intelligence in data science (ICCIDS). IEEE, pp 1–6Google Scholar
  18. 18.
    Sahu B, Mishra D (2012) A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Eng 38:27–31CrossRefGoogle Scholar
  19. 19.
    Sserwadda A, Saraċ ÖS (2017) Gene selection and classification of pancreatic microarray datasets. In: Signal processing and communications applications conference (SIU), 2017 25th. IEEE, pp 1–4Google Scholar
  20. 20.
    Tan F, Fu X, Zhang Y, Bourgeois AG (2006) Improving feature subset selection using a genetic algorithm for microarray gene expression data. In: 2006. CEC 2006. IEEE congress on evolutionary computation. IEEE, pp 2529–2534Google Scholar
  21. 21.
    Wang H, Jing X, Niu B (2017) A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data. Knowl-Based Syst 126:8–19CrossRefGoogle Scholar
  22. 22.
    Wang Y, Tetko IV, Hall MA, Frank E, Facius A, Mayer KF, Mewes HW (2005) Gene selection from microarray data for cancer classification—a machine learning approach. Comput Biol Chem 29(1):37–46CrossRefGoogle Scholar
  23. 23.
    Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626CrossRefGoogle Scholar
  24. 24.
    Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74Google Scholar
  25. 25.
    Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample microarray classification?. Bioinformatics 20(3):374–380CrossRefGoogle Scholar
  26. 26.
    Isaksson A, Wallman M, Göransson H, Gustafsson MG (2008) Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recogn Lett 29(14):1960–1965CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology GoaPondaIndia

Personalised recommendations