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Classification of cancer microarray data using a two-step feature selection framework with moth-flame optimization and extreme learning machine

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Abstract

Analysis of microarray gene expression data for the detection/classification of cancer is one of the common approaches adopted worldwide. However, many genes (features) with correlated and irrelevant information in these data sets become the bottleneck for a classification model and significantly deteriorate its performance. A large number of features with fewer samples further make the classification task more cumbersome. Several feature selection methods (both filter and wrapper) have been proposed individually to address this issue, but choosing the best one among them is an open challenge. Our objective in the present study is to simplify the search for the best feature selection method without relying completely on individual methods and propose a two-step hybrid approach. In the first step, we use an ensemble of filter-based heterogeneous feature selection methods. These selected features then undergo the second step of wrapper-based selection. We propose to use the bio-inspired method called Moth-flame optimization (MFO) with an extreme learning machine (ELM) as its fitness function in this step. The motivation for using ELM is to leverage its learning strategy with one-pass processing of samples. Using this hybrid feature selection method, we proposed a classification model for Cancer Micraoarray data, where ELM is also considered as a classifier. The work demonstrates the superiority of the proposed model over other state-of-the-art methods in classifying cancer data from four different microarray gene expression datasets. Several measurement indexes are used for the performance evaluation of models.

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Data availability

The datasets analysed during the current study are available in the [Mendeley] repository for Lung cancer, Brain cancer and Prostate cancer datasets [https://data.mendeley.com/datasets/ynp2tst2hh/4]

Colon cancer [http://csse.szu.edu.cn/staff/zhuzx/Datasets.html]

References

  1. Ab Hamid TMT, Sallehuddin R, Yunos ZM, Ali A (2021) Ensemble based filter feature selection with harmonize particle swarm optimization and support vector machine for optimal cancer classification. Mach Learn Appl 5:100054

    Google Scholar 

  2. Abdulla M, Khasawneh MT (2020) G-Forest: an ensemble method for cost-sensitive feature selection in gene expression microarrays. Artif Intell Med 108:101941

    Article  PubMed  Google Scholar 

  3. Alomari OA, Makhadmeh SN, Al-Betar MA, Alyasseri ZAA, Doush IA, Abasi AK, … Zitar RA (2021) Gene selection for microarray data classification based on gray wolf optimizer enhanced with TRIZ-inspired operators. Knowl-Based Syst 223:107034

    Article  Google Scholar 

  4. Alzaqebah M, Alrefai N, Ahmed EA, Jawarneh S, Alsmadi MK (2020) Neighborhood search methods with moth optimization algorithm as a wrapper method for feature selection problems. Int J Electr Comput Eng 10(4):3672

    Google Scholar 

  5. Arowolo MO, Abdulsalam SO, Saheed YK, Salawu MD (2016) A feature selection based on One-Way-Anova for microarray data classification. AJPAS J 3:1–6

    Google Scholar 

  6. Bishop CM (2016) Pattern recognition and machine learning. Springer

    Google Scholar 

  7. Bolón-Canedo V, Alonso-Betanzos A (2019) Ensembles for feature selection: a review and future trends. Inf Fusion 52:1–12

    Article  Google Scholar 

  8. Cao J, Lin Z (2015) Extreme learning machines on high dimensional and large data applications: a survey. Math Probl Eng 2015:1–13

    CAS  Google Scholar 

  9. Chuang LY, Yang CH, Wu KC, Yang CH (2011) A hybrid feature selec-tion method for DNA microarray data. Comput Biol Med 41(4):228–237

    Article  CAS  PubMed  Google Scholar 

  10. Dabba A, Tari A, Meftali S (2021) Hybridization of moth flame optimization algorithm and quantum computing for gene selection in microarray data. J Ambient Intell Humaniz Comput 12(2):2731–2750

    Article  Google Scholar 

  11. Dabba A, Tari A, Meftali S, Mokhtari R (2021) Gene selection and classification of microarray data method based on mutual information and moth flame algorithm. Expert Syst Appl 166:114012

    Article  Google Scholar 

  12. Ding Y, Wilkins D (2006) Improving the performance of SVM-RFE to select genes in microarray data. BMC Bioinformatics 7(2):1–8, BioMed Central

    Google Scholar 

  13. Halim Z (2021) An ensemble filter-based heuristic approach for cancerous gene expression classification. Knowl-Based Syst 234:107560

    Article  Google Scholar 

  14. Haznedar B, Arslan M T, Kalinli A (2017) Microarray gene expression cancer data. Mendeley Data, 2

  15. Jackson LA, Dyer DW (2012) Protocol for gene expression profiling using DNA microarrays in Neisseria gonorrhoeae. In: Diagnosis of sexually transmitted diseases. Humana Press, Totowa, NJ, pp 3343–3357

    Google Scholar 

  16. Khurma RA, Aljarah IB, Sharieh AH (2005) Improved moth flame optimization based on Harris hawks for genesselection. J Theoret Appl Inf Technol 98:3794–3807

    Google Scholar 

  17. Kohavi R, John GH (1997) Wrapper feature subset selection. Artif Intell 97(1-2):273–324

    Article  Google Scholar 

  18. Lazar C, Taminau J, Meganck S, Steenhoff D, Coletta A, Molter C, … Nowe A (2012) A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans Comput Biol Bioinform 9(4):1106–1119

    Article  PubMed  Google Scholar 

  19. Li C, Xu J (2019) Feature selection with the fisher score followed by the maximal clique centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma. Sci Rep 9(1):1–11

    MathSciNet  ADS  Google Scholar 

  20. Li GZ, Zeng XQ, Yang JY, Yang MQ (2007) Partial least squares-based dimension reduction with gene selection for tumour classification. In: 2007 IEEE 7th international symposium on bioinformatics and bioengineering, pp 1439–1444

    Chapter  Google Scholar 

  21. Li Y, Zhu X, Liu J (2020) An improved moth-flame optimization algorithm for engineering problems. Symmetry 12(8):1234

    Article  ADS  Google Scholar 

  22. Liang S, Ma A, Yang S, Wang Y, Ma Q (2018) A review of matched-pairs feature selection methods for gene expression data analysis. Comput Struct Biotechnol 16:88–97

    Article  CAS  Google Scholar 

  23. Lin GQ, Li LL, Tseng ML, Liu HM, Yuan DD, Tan RR (2020) An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. J Clean Prod 253:119966

    Article  Google Scholar 

  24. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  25. Mollaee M, Moattar MH (2016) A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification. Biocybern Biomed Eng 36(3):521–529

    Article  Google Scholar 

  26. Muduli D, Dash R, Majhi B (2020) Automated breast cancer detection in digital mammograms: a moth flame optimization based ELM approach. Biomedical Signal Processing and Control 59:101912

    Article  Google Scholar 

  27. Nadimi-Shahraki MH, Banaie-Dezfouli M, Zamani H, Taghian S, Mirjalili S (2021) B-MFO: a binary moth-flame optimization for feature selection from medical datasets. Computers 10(11):136

    Article  Google Scholar 

  28. Pashaei E, Ozen M, Aydin N (2016) Gene selection and classification approach for microarray data based on random forest ranking and BBHA. In: 2016 IEEE-EMBS international conference on biomedical and health informatics (BHI). IEEE, pp 308–311

    Chapter  Google Scholar 

  29. Prakash J, Kankar PK (2020) Health prediction of hydraulic cooling circuit using deep neural network with ensemble feature ranking technique. Measurement 151:107225

  30. Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinforma 18(1):1–14

    Article  Google Scholar 

  31. Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. bioinformatics 23(19):2507–2517

    Article  CAS  PubMed  Google Scholar 

  32. Santos V, Datia N, Pato MPM (2014) Ensemble feature ranking applied to medical data. Proc Technol 17(2014):223–230

    Article  Google Scholar 

  33. Singh N, Singh P (2021) A hybrid ensemble-filter wrapper feature se-lection approach for medical data classification. Chemom Intell Lab Syst 217:104396

    Article  CAS  Google Scholar 

  34. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame op-timization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Article  Google Scholar 

  35. Wang A, Liu H, Yang J, Chen G (2022) Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data. Comput Biol Med:105208

  36. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  37. Zhang G, Hou J, Wang J, Yan C, Luo J (2020) Feature selection for microarray data classification using hybrid information gain and a modified binary krill herd algorithm. Interdiscip Sci Comput Life Sci 12(3):288–301

    Article  CAS  Google Scholar 

  38. Zhu Z, Ong YS, Dash M (2007) Markov blanket-embedded genetic algorithm for gene selection. Pattern Recogn 40(11):3236–3248

    Article  ADS  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India

    Swati Sucharita & Barnali Sahu

  2. Department of Computer Application, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India

    Tripti Swarnkar

  3. Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, India

    Saroj K. Meher

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  1. Swati Sucharita
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  2. Barnali Sahu
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  4. Saroj K. Meher
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Correspondence to Swati Sucharita.

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Sucharita, S., Sahu, B., Swarnkar, T. et al. Classification of cancer microarray data using a two-step feature selection framework with moth-flame optimization and extreme learning machine. Multimed Tools Appl 83, 21319–21346 (2024). https://doi.org/10.1007/s11042-023-16353-2

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  • DOI: https://doi.org/10.1007/s11042-023-16353-2

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