Skip to main content
Erschienen in: Medical & Biological Engineering & Computing 12/2019

12.11.2019 | Original Article

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

verfasst von: Kaveri Chatra, Venkatanareshbabu Kuppili, Damodar Reddy Edla, Ajeet Kumar Verma

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 12/2019

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat 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 58–62 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 58–62
2.
Zurück zum Zitat 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– 11 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– 11
3.
Zurück zum Zitat 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 185–189 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 185–189
4.
Zurück zum Zitat 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 451–455 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 451–455
5.
Zurück zum Zitat Chuang LY, Chang HW, Tu CJ, Yang CH (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38CrossRef Chuang LY, Chang HW, Tu CJ, Yang CH (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38CrossRef
6.
Zurück zum Zitat 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 1–6 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 1–6
7.
Zurück zum Zitat Dougherty ER (2001) Small sample issues for microarray-based classification. Compar Funct Genom 2(1):28–34CrossRef Dougherty ER (2001) Small sample issues for microarray-based classification. Compar Funct Genom 2(1):28–34CrossRef
8.
Zurück zum Zitat Fahrudin TM, Syarif I, Barakbah AR (2016) Ant colony algorithm for feature selection on microarray datasets. In: 2016 international electronics symposium (IES). IEEE, pp 351–356 Fahrudin TM, Syarif I, Barakbah AR (2016) Ant colony algorithm for feature selection on microarray datasets. In: 2016 international electronics symposium (IES). IEEE, pp 351–356
9.
Zurück zum Zitat 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):531–537CrossRef 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):531–537CrossRef
10.
Zurück zum Zitat 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 1–6 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 1–6
11.
Zurück zum Zitat 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 985–990 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 985–990
12.
Zurück zum Zitat 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):1106–1119CrossRef 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):1106–1119CrossRef
13.
Zurück zum Zitat 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):198CrossRef 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):198CrossRef
14.
Zurück zum Zitat 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 825–830 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 825–830
15.
Zurück zum Zitat Lu Y, Han J (2003) Cancer classification using gene expression data. Inf Syst 28(4):243–268CrossRef Lu Y, Han J (2003) Cancer classification using gene expression data. Inf Syst 28(4):243–268CrossRef
16.
Zurück zum Zitat Lv J, Peng Q, Chen X, Sun Z (2016) A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst Appl 59:13–19CrossRef Lv J, Peng Q, Chen X, Sun Z (2016) A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst Appl 59:13–19CrossRef
17.
Zurück zum Zitat 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 1–6 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 1–6
18.
Zurück zum Zitat Sahu B, Mishra D (2012) A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Eng 38:27–31CrossRef Sahu B, Mishra D (2012) A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Eng 38:27–31CrossRef
19.
Zurück zum Zitat 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 1–4 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 1–4
20.
Zurück zum Zitat 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 2529–2534 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 2529–2534
21.
Zurück zum Zitat 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 126:8–19CrossRef 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 126:8–19CrossRef
22.
Zurück zum Zitat 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):37–46CrossRef 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):37–46CrossRef
23.
Zurück zum Zitat Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626CrossRef Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626CrossRef
24.
Zurück zum Zitat Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74 Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74
25.
Zurück zum Zitat Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample microarray classification?. Bioinformatics 20(3):374–380CrossRef Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample microarray classification?. Bioinformatics 20(3):374–380CrossRef
26.
Zurück zum Zitat Isaksson A, Wallman M, Göransson H, Gustafsson MG (2008) Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recogn Lett 29(14):1960–1965CrossRef Isaksson A, Wallman M, Göransson H, Gustafsson MG (2008) Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recogn Lett 29(14):1960–1965CrossRef
Metadaten
Titel
Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function
verfasst von
Kaveri Chatra
Venkatanareshbabu Kuppili
Damodar Reddy Edla
Ajeet Kumar Verma
Publikationsdatum
12.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 12/2019
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-019-02043-5

Weitere Artikel der Ausgabe 12/2019

Medical & Biological Engineering & Computing 12/2019 Zur Ausgabe