Multiclass Cancer Diagnosis using Firefly Algorithm and K- Nearest Neighbor

Abstract

Abstract - Among a large number of genes in microarray data sets that characterize the samples, many of them may be irrelevant

to the learning tasks. Thus there is a need for reliable methods for gene representation, reduction, and selection, to speed up the

processing rate, improve the classification accuracy, and to avoid incomprehensibility due to the high number of genes investigated.

Classifying multiclass data sets is usually more difficult than classifying microarray datasets with only two classes. In this paper,

we propose a new gene selection and classification strategy based on Firefly Algorithm (FFA) and K- Nearest Neighbor (KNN),

suitable for multiclass microarray data sets. This approach is associated with Kruskal-test pre-filtering technique. The FFA is

utilized to evolve gene subsets whose fitness is evaluated by a KNN classifier with leave-one-out-cross-validation (LOOCV)

schema. The experimental results on three multiclass high-dimensional data sets show that the proposed method simplifies gene

signatures effectively and obtains approximately higher classification accuracy compared to the best previously published results.

Keywords

• Gene selection,firefly algorithm,kruskal-test,k- nearest neighbor

References

1. References [1] L. Y. Chuang, H. W. Chang, C. J. Tu, and C. H. Yang, "Improved binary PSO for feature selection using gene expression data," Computational Biology and Chemistry, vol. 32, pp. 29-38, 2008. [2] B. Tran, B. Xue, and M. Zhang, "Improved PSO for Feature Selection on High-Dimensional Datasets," Springer International Publishing Switzerland, pp. 503–515, 2014. [3] E. Pashaei, M. Ozen, and N. Aydin, "A Novel Gene Selection Algorithm for cancer identification based on Random Forest and Particle Swarm Optimization," presented at the Proceedings of 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Niagara Falls, Canada, 2015. [4] Y. Hualong, G. Guochang, L. Haibo, S. Jing, and Z. Jing, "A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection," Genomics Proteomics Bioinformatics, vol. 7, pp. 200–208, 2009 Dec. [5] E. Pashaei and N. Aydin, "Binary black hole algorithm for feature selection and classification on biological data," Applied Soft Computing, vol. 56, pp. 94-106, 2017. [6] A. Srivastava, S. Chakrabarti, S. Das, S. Ghosh, and V. K. Jayaraman, "Hybrid Firefly Based Simultaneous Gene Selection and Cancer Classification Using Support Vector Machines and Random Forests," in Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), India, 04 December 2012, pp. 485-494. [7] X. S. Yang, "Firefly algorithm," Nature-Inspired Metaheuristic Algorithms, pp. 79-90, 2008. [8] B. CRAWFORD, R. SOTO, M. OLIVARES-SUAREZ, W. PALMA, F. PAREDES, E. OLGU´IN, et al., "A Binary Coded Firefly Algorithm that Solves the Set Covering Problem," ROMANIAN JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, vol. 17, pp. 252–264, 2014. [9] X.-S. Yang and X. He, "Firefly Algorithm: Recent Advances And Applications," Int. J. Swarm Intelligence, vol. 1, pp. 36-50, 2013. [10] R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, "Class prediction by nearest shrunken centroids with applications to DNA microarrays. ," Statistical Science, vol. 18, pp. 104-117, 2003. [11] M. Slawski, M. Daumer, and A. L. Boulesteix, "CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data," BMC Bioinformatics, vol. 9, 2008. [12] A. J. Ferreira and M. r. A. T. Figueiredo, "An unsupervised approach to feature discretization and selection," Pattern Recognition, vol. 45, pp. 3048–3060, 2012. [13] L. Y. Chuang, C. H. Yang, and C. H. Yang, "Tabu search and binary particle swarm optimization for feature selection using microarray data," J Comput Biol, vol. 16, pp. 1689–703, 2009. [14] M. S. Mohamad, S. Omatu, S. Deris, M. Yoshioka, A. Abdullah, and Z. Ibrahim, "An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes," Algorithms Mol Biol, vol. 8, pp. 1-11, 2013.