Abstract
Advances in cancer diagnosis methods have led to the development of highly accurate, detailed and voluminous data. Unfortunately, high dimensional data often leads to poor accuracy and high processing time. Swarm intelligence based feature selection methods have been highly efficient in the biomedical domain, which motivates the exploration of more adaptive and newer wrapper based methods such as the Firefly algorithm. This paper explores the inclusion of a penalty function to the existing fitness function promoting the Binary Firefly Algorithm to drastically reduce the feature set to an optimal subset, and shows an increase in both classification accuracy as well as feature reduction using a Random Forest classifier for the diagnosis of Breast, Cervical and Hepatocellular Carcinoma - Liver Cancer by the proposed method in comparison to other contemporary methods such as those based on Deep Learning, Information Gain and others.
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References
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)
Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14
Soto, C., Valdez, F., Castillo, O.: A review of dynamic parameter adaptation methods for the firefly algorithm. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems. SCI, vol. 667, pp. 285–295. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47054-2_19
Danaei, G., Vander Hoorn, S., Lopez, A.D., Murray, C.J., Ezzati, M., Comparative Risk Assessment collaborating group (Cancers), et al.: Causes of cancer in the world: comparative risk assessment of nine behavioural and environmental risk factors. Lancet 366(9499), 1784–1793 (2005)
Bruix, J., Sherman, M.: Management of hepatocellular carcinoma: an update. Hepatology 53(3), 1020–1022 (2011)
Brinton, L.A., Hamman, R.F., Huggins, G.R., Lehman, H.F., Levine, R.S., Mailin, K., Fraumeni Jr., J.F.: Sexual and reproductive risk factors for invasive squamous cell cervical cancer. J. Natl Cancer Inst. 79(1), 23–30 (1987)
Slattery, M.L., Robison, L.M., Schuman, K.L., French, T.K., Abbott, T.M., Overall, J.C., Gardner, J.W.: Cigarette smoking and exposure to passive smoke are risk factors for cervical cancer. Jama 261(11), 1593–1598 (1989)
Bosch, F., Munoz, N., De Sanjosé, S., Izarzugaza, I., Gili, M., Viladiu, P., Tormo, M., Moreo, P., Ascunce, N., Gonzalez, L., et al.: Risk factors for cervical cancer in Colombia and Spain. Int. J. Cancer 52(5), 750–758 (1992)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)
Vafaie, H., De Jong, K.: Genetic algorithms as a tool for feature selection in machine learning. In: Proceedings of the Fourth International Conference on Tools with Artificial Intelligence, TAI 1992, pp. 200–203. IEEE (1992)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Cruz, J.A., Wishart, D.S.: Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2, 117693510600200030 (2006). https://doi.org/10.1177/117693510600200030
Chen, H.L., Yang, B., Liu, J., Liu, D.Y.: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl. 38(7), 9014–9022 (2011)
Kabir, M.M., Shahjahan, M., Murase, K.: A new hybrid ant colony optimization algorithm for feature selection. Expert Syst. Appl. 39(3), 3747–3763 (2012)
Rodrigues, D., Pereira, L.A., Almeida, T., Papa, J.P., Souza, A., Ramos, C.C., Yang, X.S.: BCS: a binary cuckoo search algorithm for feature selection. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 465–468. IEEE (2013)
Nakamura, R.Y., Pereira, L.A., Costa, K., Rodrigues, D., Papa, J.P., Yang, X.S.: BBA: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 291–297. IEEE (2012)
Łukasik, S., Żak, S.: Firefly algorithm for continuous constrained optimization tasks. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 97–106. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04441-0_8
Basu, B., Mahanti, G.: Thinning of concentric two-ring circular array antenna using fire fly algorithm. Scientia Iranica 19(6), 1802–1809 (2012)
Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87(23), 9193–9196 (1990)
Zhang, J.: Selecting typical instances in instance-based learning. In: Machine Learning Proceedings 1992, pp. 470–479. Elsevier (1992)
Fernandes, K., Cardoso, J.S., Fernandes, J.: Transfer learning with partial observability applied to cervical cancer screening. In: Alexandre, L.A., Salvador Sánchez, J., Rodrigues, J.M.F. (eds.) IbPRIA 2017. LNCS, vol. 10255, pp. 243–250. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58838-4_27
Santos, M.S., Abreu, P.H., García-Laencina, P.J., Simão, A., Carvalho, A.: A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients. J. Biomed. Inform. 58, 49–59 (2015)
Fister, I., Fister Jr., I., Yang, X., Brest, J.: A comprehensive review of firefly algorithms. CoRR abs/1312.6609 (2013). http://arxiv.org/abs/1312.6609
Mirjalili, S., Lewis, A.: S-shaped versus v-shaped transfer functions for binary particle swarm optimization 9, 1–14 (2013)
Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28(4), 459–471 (2007)
Bonyadi, M.R., Michalewicz, Z.: Particle swarm optimization for single objective continuous space problems: a review (2017)
Donoho, D.L., et al.: High-dimensional data analysis: the curses and blessings of dimensionality. AMS Math Chall. Lect. 1, 32 (2000)
Li, W., Shi, T., Liao, G., Yang, S.: Feature extraction and classification of gear faults using principal component analysis. J. Qual. Maint. Eng. 9(2), 132–143 (2003)
Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M.N., Salcedo-Sanz, S., Geem, Z.W.: A survey on applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26(8), 1818–1831 (2013)
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)
Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)
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Sawhney, R., Mathur, P., Shankar, R. (2018). A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_30
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