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Feature selection and computational optimization in high-dimensional microarray cancer datasets via InfoGain-modified bat algorithm

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Abstract

Achieving a satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task, most importantly in heterogeneous multimedia data. One of the major drawbacks in cancer study is recognizing informative genes from thousands of available genes in microarray data. Traditional feature selection algorithms have failed to scale on large space data like microarray data. Therefore, an effective feature selection algorithm is required to explore the most significant subset of genes by removing non-predictive genes from the dataset without compromising the accuracy of the classification algorithm. The study proposed an information Gain – Modified Bat Algorithm (InfoGain-MBA) features selection model for selecting relevant and informative features from high dimensional Microarray cancer datasets and evaluate the approach with four classifiers - C4.5, Decision Tree, Random Forest and classification and regression tree (CART). The results obtained show that the proposed approach is promising for the classification of microarray cancer data. The random forest has 100% accuracy with few genes in all seven datasets used. Further investigations were also conducted to determine the optimal threshold for each of the datasets.

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References

  1. Abeer MM, Basma AM, El-Sayed ME, Abdel-Badeeh MS (2013) Applying a statistical technique for the discovery of differentially expressed genes in microarray data. Int Conf. on Recent Advances in Circuits, Systems, Telecommunications and Control, pp 220–227.

  2. Aitkenhead MJ (2008) A co-evolving decision tree classification method. Expert Syst Appl 34:18–25. https://doi.org/10.1016/j.eswa.2006.08.008

    Article  Google Scholar 

  3. Alomari OA, Khader AT, Al-Betar MA, Abualigah LM (2017) MRMR BA: a hybrid gene selection algorithm for cancer classification. J Theor Appl Inf Technol 95(12):2610–2618

    Google Scholar 

  4. Alshamlan HM, Badr GH, Alohali YA (2015) Genetic bee Colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput Biol Chem 56:49–60. https://doi.org/10.1016/j.compbiolchem.2015.03.001

    Article  Google Scholar 

  5. Bennet J, Ganaprakasam C, Kumar N (2015) A hybrid approach for gene selection and classification using support vector machine. Int Arab J Inf Technol 12(6A):695–700

  6. Biau G (2012) Analysis of a random forests model. J Mach Learn Res 13:1063–1095

    MathSciNet  MATH  Google Scholar 

  7. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Distributed feature selection: an application to microarray data classification. Appl Soft Comput J 30:136–150. https://doi.org/10.1016/j.asoc.2015.01.035

    Article  Google Scholar 

  8. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, vol 432. Wadsworth International Group, Belmont

    MATH  Google Scholar 

  9. Cao J, Zhang L, Wang B, Li F, Yang J (2015) A fast gene selection method for multi-cancer classification using multiple support vector data description. J Biomed Inform 53:381–389

    Article  Google Scholar 

  10. Chormunge S, Jena S (2018) Correlation based feature selection with clustering for high dimensional data. J Electr Syst Inf Technol 5(3):542–549

    Article  Google Scholar 

  11. Chuang LY, Yang CH, Li JC (2011) A hybrid BPSO-CGA approach for gene selection and classification of microarray data. J Comput Biol 19:1–14

    MathSciNet  Google Scholar 

  12. Dashtban M, Balafar M, Suravajhala P (2018) Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics 110(1):10–17. https://doi.org/10.1016/j.ygeno.2017.07.010

    Article  Google Scholar 

  13. De Caigny A, Coussement K, De Bock KW (2018) A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur J Oper Res 269(2):760–772. https://doi.org/10.1016/j.ejor.2018.02.009

    Article  MathSciNet  MATH  Google Scholar 

  14. Dev J, Dash SK, Dash S, Swain M (2012) A classification technique for microarray gene expression data using PSO-FLANN. Int J Comput Sci Eng 4(09):1534–1539

    Google Scholar 

  15. Doddipalli L, Rani KU (2016) Ensemble decision tree classifier for breast Cancer data. Int J Inf Technol Converg Serv 2(1):16–24. https://doi.org/10.5121/ijitcs.2012.2103

    Article  Google Scholar 

  16. Doreswamy H, Salma UM (2016) A binary bat inspired algorithm for the classification of breast Cancer data. Int J Soft Comput Intell Appl 5(2/3):1–21

    Google Scholar 

  17. Ebrahimpour MK, Nezamabadi-Pour H, Eftekhari M (2018) CCFS: a cooperating coevolution technique for large scale feature selection on microarray datasets. Comput Biol Chem 73:171–178

    Article  Google Scholar 

  18. El Akadi A, Amine A, El Ouardighi A, Aboutajdine D (2011) A two-stage gene selection scheme utilizing MRMR filter and GA wrapper. Knowl Inf Syst 26(3):487–500

    Article  Google Scholar 

  19. Fatemeh VS, Sara M, Mohammad HM (2016) A hybrid gene selection approach for microarray data classification using cellular learning automata and ant Colony optimization. Genomics 107:231–238

    Article  Google Scholar 

  20. Forsati R, Moayedikia A, Jensen R, Shamsfard M, Meybodi MR (2014) Enriched ant Colony optimization and its application in feature selection. Neurocomputing 142:354–371

    Article  Google Scholar 

  21. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1. Springer Series in Statistics, New York

    MATH  Google Scholar 

  22. Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural computing and applications. Neural Comput & Applic 22(6):1239–1255

    Article  Google Scholar 

  23. Geetha R, Sivasubramanian S, Kaliappan M, Vimal S, Annamalai S (2019) Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J Med Syst 43(9):286

    Article  Google Scholar 

  24. Genuer R, Poggi J-M, Tuleau C (2008) Random Forests: some methodological insights. Retrieved January 18, 2020, from https://doi.org/10.48550/arXiv.0811.3619

  25. Ghorai S, Mukherjee A, Sengupta S, Dutta PK (2010) Cancer classification from gene expression data by NPPC ensemble. IEEE/ACM Trans Comput Biol Bioinforma 8(3):659–671

    Article  Google Scholar 

  26. Griffin DR, Webster FA, Michael CR (1960) The echolocation of flying insects by bats. Animal Behaviour, 8(3):141–154.

  27. Gunavathi C, Premalatha K (2015) Cuckoo search optimisation for feature selection in cancer classification: a new approach. Int J Data Min Bioinform 13(3):248–265

    Article  Google Scholar 

  28. Hall MA, Smith LA (1998) Practical feature subset selection for machine learning. In C. McDonald(Ed.), Computer Science 98 Proceedings of the 21st Australasian Computer Science Conference ACSC’98, Perth, 4-6 February 1998 (pp 181–191). Berlin: Springer.

  29. Hambali MA, Gbolagade MD (2016) Ovarian cancer classification using hybrid synthetic minority over-sampling technique and neural network. J Adv Comput Res 7(4):109–124

    Google Scholar 

  30. Hambali M, Saheed Y, Oladele T, Gbolagade M (2019) ADABOOST ensemble algorithms for breast cancer classification. J Adv Comput Res 10(2):31–52. http://jacr.iausari.ac.ir/article_663924.html. Accessed 18 Jan 2020

  31. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Morgan Kaufmann, 340:94104–3205.

  32. Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinforma 2015:1–13. Retrieved January 18, 2020, from https://doi.org/10.1155/2015/198363

  33. Kabir MM, Shahjahan M, Murase K (2012) A new hybrid ant colony optimization algorithm for feature selection. Expert Syst Appl 39(3):3747–3763

    Article  Google Scholar 

  34. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22

    Google Scholar 

  35. Lin S-W, Chen S-C (2012) Parameter determination and feature selection for C4. 5 algorithm using scatter search approach. Soft Comput 16(1):63–75

    Article  Google Scholar 

  36. Lin W-Z, Fang J-A, Xiao X, Chou K-C (2011) iDNA-Prot: identification of DNA binding proteins using random forest with grey model. PLoS One 6(9):e24756

    Article  Google Scholar 

  37. Lønning PE, Sørlie T, Børresen-Dale A-L (2005) Genomics in breast cancer—therapeutic implications. Nat Clin Pract Oncol 2(1):26–33

    Article  Google Scholar 

  38. Mahmoud AM, Maher BA (2014) A hybrid reduction approach for enhancing cancer classification of microarray data. Int J Adv Res Artif Intell 3(10):1–10

    Google Scholar 

  39. Maldonado S, López J (2018) Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification. Appl Soft Comput 67:94–105

    Article  Google Scholar 

  40. Martens H (2001) Reliable and relevant modelling of real world data: a personal account of the development of PLS regression. Chemom Intell Lab Syst 58(2):85–95

    Article  Google Scholar 

  41. Martín-Merino M, De Las Rivas J (2009) Improving k-nn for human cancer classification using the gene expression profiles. In: International Symposium on Intelligent Data Analysis, pp. 107–118

  42. Metzner W (1991) Echolocation behaviour in bats. Sci Prog Edinburgh 75(298):453–465. http://www.files/27/ADABOOSTEnsembleAlgorithmsforBreastCancerClassification.ris. Accessed 18 Jan 2020

  43. Mishra S, Shaw K, Mishra D (2012) A new meta-heuristic bat inspired classification approach for microarray data. Procedia Technol 4:802–806

    Article  Google Scholar 

  44. Mitchell TM (1997) Machine learning

  45. Moody J, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1(2):281–294

    Article  Google Scholar 

  46. Motieghader H, Najafi A, Sadeghi B, Masoudi-Nejad A (2017) A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Informatics Med Unlocked 9:246–254

    Article  Google Scholar 

  47. Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. Braz Symp Comput Graph Image Process 291–297. Retrieved January 18, 2020, from https://doi.org/10.1109/SIBGRAPI.2012.47

  48. Narayanan A, Keedwell EC, Olsson B (2002) Artificial intelligence techniques for bioinformatics. Appl Bioinforma 1:191–222

    Google Scholar 

  49. Nguyen DV, Rocke DM (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18(1):39–50

    Article  Google Scholar 

  50. Nguyen DV, Rocke DM (2002) Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics 18(9):1216–1226

    Article  Google Scholar 

  51. Panigrahi R, Borah S (2018) Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. Procedia Comput Sci 132:323–332

    Article  Google Scholar 

  52. Panigrahi PP, Singh TR (2013) Computational studies on Alzheimer’s disease associated pathways and regulatory patterns using microarray gene expression and network data: revealed association with aging and other diseases. J Theor Biol 334:109–121

    Article  MATH  Google Scholar 

  53. Pirooznia M, Yang JY, Yang MQ, Deng Y (2008) A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics 9(S1):S13

    Article  Google Scholar 

  54. Polat K, Güneş S (2009) A novel hybrid intelligent method based on C4. 5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst Appl 36(2):1587–1592

    Article  Google Scholar 

  55. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Article  Google Scholar 

  56. Rajeswari P, Reena GS (2011) Human liver cancer classification using microarray gene expression data. Int J Comput Appl 34(6):25–37

    Google Scholar 

  57. Rana MM, Ahmed K (2020) Feature selection and biomedical signal classification using minimum redundancy maximum relevance and artificial neural network. In: Proceedings of International Joint Conference on Computational Intelligence, pp. 207–214

  58. Rangasamy M (2009) An efficient statistical model based classification algorithm for classifying cancer gene expression data with minimal gene subsets. Int J Cyber Soc Educ 2(2):51–66

    Google Scholar 

  59. Revathy N, Amalraj R (2011) Accurate cancer classification using expressions of very few genes. Int J Comput Appl 14(4):19–22

    Google Scholar 

  60. Rodrigues D, Pereira LAM, Nakamura RYM, Costa KAP, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst Appl 41(5):2250–2258

    Article  Google Scholar 

  61. Saeid MM, Nossair ZB, Saleh MA (2020) A microarray cancer classification technique based on discrete wavelet transform for data reduction and genetic algorithm for feature selection. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), pp. 857–861

  62. Sahu B, Mishra D (2012) A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Eng 38:27–31

    Article  Google Scholar 

  63. Schnitzler H-U, Kalko EKV (2001) Echolocation by insect-eating bats: we define four distinct functional groups of bats and find differences in signal structure that correlate with the typical echolocation tasks faced by each group. Bioscience 51(7):557–569

    Article  Google Scholar 

  64. Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249

    Article  Google Scholar 

  65. Selvaraj S, Natarajan J (2011) Microarray data analysis and mining tools. Bioinformation 6(3):95

    Article  Google Scholar 

  66. Shafi ASM, Molla MMI, Jui JJ, Rahman MM (2020) Detection of colon cancer based on microarray dataset using machine learning as a feature selection and classification techniques. SN Appl Sci 2(7):1–8

    Article  Google Scholar 

  67. Shreem SS, Abdullah S, Nazri MZA (2014) Hybridising harmony search with a Markov blanket for gene selection problems. Inf Sci (NY) 258:108–121

    Article  MathSciNet  Google Scholar 

  68. Sulaiman A, Akinbowale B, Ronke B, Moshood H (2015) Comparative analysis of decision tree algorithms for predicting undergraduate students’ performance in computer programming. J Adv Sci Res Appl 2(20):79–92

  69. Suresh A, Udendhran R, Balamurgan M (2020) Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Comput 24:7947–7953

  70. Swathi S, Babu GA, Sendhilkumar R, Bhukya SN (2012) Performance of ART1 network in the detection of breast cancer. In: Proceedings of International Conference on Computer design and Engineering (ICCDE 2012), vol. 49, pp. 100–105.

  71. Tang R, Fong S, Yang X-S, Deb S (2012) Integrating nature-inspired optimization algorithms to K-means clustering. In: Seventh International Conference on Digital Information Management (ICDIM 2012), pp. 116–123.

  72. Veerabhadrappa, Rangarajan L (2010) Bi-level dimensionality reduction methods using feature selection and feature extraction. Int J Comput Appl 4(2):33–38

    Google Scholar 

  73. Vieira SM, Mendonça LF, Farinha GJ, Sousa JMC (2013) Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Appl Soft Comput 13(8):3494–3504

    Article  Google Scholar 

  74. Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math vol. 2013. Retrieved January 18, 2020, from https://doi.org/10.1155/2013/696491

  75. Wang L, Chu F, Xie W (2007) Accurate cancer classification using expressions of very few genes. IEEE/ACM Trans Comput Biol Bioinforma 4(1):40–53

    Article  Google Scholar 

  76. Yang X-S (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3(5):267–274. https://doi.org/10.1504/IJBIC.2011.042259

    Article  Google Scholar 

  77. Yang X, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483. Retrieved January 18, 2020, from https://doi.org/10.1108/02644401211235834

  78. Yang XS, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141. https://doi.org/10.1504/IJBIC.2013.055093

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

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Acknowledgments

This research has been financially supported in part by Tertiary Education Trust Fund (TETFUND) with Reference FUW/REG/T.5/VOL.1/T11. We also acknowledge the support of Ministry of Education of the People’s Republic of China.

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Tertiary Education Trust Fund (TETFUND). Ministry of Education of the People’s Republic of China.

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Authors and Affiliations

  1. Department of Computer Science, Federal University Wukari, Wukari, Taraba State, Nigeria

    Moshood A. Hambali

  2. Department of Computer Science, University of Ilorin, Ilorin, Kwara State, Nigeria

    Tinuke O. Oladele & Kayode S. Adewole

  3. National Yunlin University of Science and Technology, Douliu, 633102, Taiwan

    Arun Kumar Sangaiah

  4. School of information Science and Technology, Yunnan Normal University, Kunming, China

    Wei Gao

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Correspondence to Arun Kumar Sangaiah.

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Appendix

Appendix

Table 13 Computation time of MBB algorithm against BatSize for Leukemia_4c dataset with 118 features
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Table 14 Statistic of estimated error rate per class for all the datasets
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Hambali, M.A., Oladele, T.O., Adewole, K.S. et al. Feature selection and computational optimization in high-dimensional microarray cancer datasets via InfoGain-modified bat algorithm. Multimed Tools Appl 81, 36505–36549 (2022). https://doi.org/10.1007/s11042-022-13532-5

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