ROADEF 2017
Free Access
Issue
RAIRO-Oper. Res.
Volume 53, Number 1, January–March 2019
ROADEF 2017
Page(s) 269 - 288
DOI https://doi.org/10.1051/ro/2018059
Published online 15 February 2019
  • S. Agarwal, R. Rajesh and P. Ranjan, FRBPSO: a Fuzzy rule based binary PSO for feature selection. Proc. Nat. Acad. Sci. India Sec. A: Phys. Sci. 87 (2017) 221–233. [CrossRef] [Google Scholar]
  • E. Alba, J. Garcia-Nieto, L. Jourdan and E.G. Talbi, Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. In: IEEE Congress on Evolutionary Computation, 2007. CEC 2007. IEEE (2007, September) 284–290. [CrossRef] [Google Scholar]
  • A.A. Alizadeh, M.B. Eisen, R.E. Davis, C. Ma, I.S. Lossos, A. Osenwald, et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403 (2000) 503. [CrossRef] [PubMed] [Google Scholar]
  • E. Amaldi and V. Kann, On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor. Comput. Sci. 209 (1998) 237–260. [Google Scholar]
  • J. Apolloni, G. Leguizamón and E. Alba, Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl. Soft Comput. 38 (2016) 922–932. [Google Scholar]
  • K.H. Chen, K.J. Wang, K.M. Wang and M.A. Angelia, Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data. Appl. Soft Comput. 24 (2014) 773–780. [Google Scholar]
  • Y.M. Chiang, H.M. Chiang and S.Y. Lin, The application of ant colony optimization for gene selection in microarray-based cancer classification. In: International Conference on Machine Learning and Cybernetics, 2008. IEEE (2008) 4001–4006. [CrossRef] [Google Scholar]
  • L.Y. Chuang, H.W. Chang, C.J. Tu and C.H. Yang, Improved binary PSO for feature selection using gene expression data. Comput. Biol. Chem. 32 (2008) 29–38. [PubMed] [Google Scholar]
  • 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. 16 (2009) 1689–1703. [PubMed] [Google Scholar]
  • C. Cortes and V. Vapnik, Support-vector networks. Mach. Learn. 20 (1995) 273–297. [Google Scholar]
  • T. Cover and P. Hart, Nearest neighbor pattern classification. IEEE Trans. Info. Theory 13 (1967) 21–27. [NASA ADS] [CrossRef] [Google Scholar]
  • M. Dashtban, M. Balafar and P. Suravajhala, Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics 110 (2018) 10–17. [CrossRef] [PubMed] [Google Scholar]
  • E. Fix and J.L. Hodges Jr, Discriminatory Analysis-Nonparametric Discrimination: Consistency Properties. California Univ Berkeley, Berkeley (1951). [Google Scholar]
  • T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard and M. Gaasenbeek, et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531–537. [Google Scholar]
  • Y. Guermeur, SVM multiclasses, théorie et applications. Habilitation à diriger des recherches. UHP (2007). [Google Scholar]
  • Q. Gu, Z. Li and J. HanGeneralized fisher score for feature selection. Preprint arXiv: 1202.3725 (2012). [Google Scholar]
  • C.W. Hsu, C.C. Chang and C.J. Lin, A practical guide to support vector classification. Available at: http://www.csie.ntu.edu.tw/ cjlin/ papers/guide/guide.pdf (2003). [Google Scholar]
  • H.Y. Huang and C.J. Lin, Linear and kernel classification: when to use which? In: Proc. of the 2016 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2016) 216–224. [CrossRef] [Google Scholar]
  • P. Jafari and F. Azuaje, An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med. Info. Decis. Mak. 6 (2006) 27. [CrossRef] [Google Scholar]
  • J. Kennedy and R. Eberhart, PSO optimization. In: Proc. IEEE Int. Conf. Neural Networks. IEEE Service Center, Piscataway, NJ 4 (1995) 1941–1948. [Google Scholar]
  • J. Kennedy and R.C. Eberhart, A discrete binary version of the particle swarm algorithm. In: Systems, Man, and Cybernetics, 1997. IEEE International Conference on Computational Cybernetics and Simulation. IEEE 5 (1997) 4104–4108. [CrossRef] [Google Scholar]
  • K. Kira and L.A. Rendell, A practical approach to feature selection. In: Proc. of the Ninth International Workshop on Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1992) 249–256. [Google Scholar]
  • R. Kohavi and G.H. John, Wrappers for features subset selection. Artif. Intell. 97 (1997) 273–324. [Google Scholar]
  • I. Kononenko, Estimating attributes: analysis and extensions of RELIEFIn: European Conference on Machine Learning. Springer, Berlin, Heidelberg (1994) 171–182. [Google Scholar]
  • B. Kumari and T. Swarnkar, Filter versus wrapper feature subset selection in large dimensionality micro array: a review. Int. J. Comput. Sci. Inf. Technol. 2 (2011) 1048–1053. [Google Scholar]
  • C.M. Lai, W.C. Yeh and C.Y. Chang, Gene selection using information gain and improved simplified swarm optimization. Neurocomputing 218 (2016) 331–338. [Google Scholar]
  • C.P. Lee and Y. Leu, A novel hybrid feature selection method for microarray data analysis. Appl. Soft Comput. 11 (2011) 208–213. [Google Scholar]
  • Y. Li, G. Wang, H. Chen, L. Shi and L. Qin, An ant colony optimization based dimension reduction method for high-dimensional datasets. J. Bionic Eng. 10 (2013) 231–241. [Google Scholar]
  • S. Li, X. Wu and M. Tan, Gene selection using hybrid particle swarm optimization and genetic algorithm. Soft Comput. 12 (2008) 1039–1048. [Google Scholar]
  • H. Liu and H. Motoda, Feature selection for knowledge discovery and data mining. In Vol. 454. Springer Science Business Media (2012). [Google Scholar]
  • D. Mishra and B. Sahu, Feature selection for cancer classification: a signal-to-noise ratio approach. Int. J. Sci. Eng. Res. 2 (2011) 1–7. [Google Scholar]
  • 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. Algorithm Mol. Biol. 8 (2013) 15. [CrossRef] [Google Scholar]
  • S.K. Pati, A.K. Das, A. Ghosh, Gene selection using multi-objective genetic algorithm integrating cellular automata and rough set theory. In: International Conference on Swarm, Evolutionary, and Memetic Computing. Springer, Cham (2013) 144–155. [CrossRef] [Google Scholar]
  • A.C. Pease, D. Solas, E.J. Sullivan, M.T. Cronin, C.P. Holmes and S.P. Fodor, Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc. Nat. Acad. Sci. 91 (1994) 5022–5026. [CrossRef] [Google Scholar]
  • J.C. Platt, N. Cristianini and J. Shawe-Taylor, Large margin DAGs for multiclass classification. In: Proc. of Advances in neural information processing systems (2000) 547–553. [Google Scholar]
  • F.V. Sharbaf, S. Mosafer and M.H. Moattar, A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics 107 (2016) 231–238. [CrossRef] [PubMed] [Google Scholar]
  • S.S. Shreem, S. Abdullah, M.Z.A. Nazri and M. Alzaqebah, Hybridizing ReliefF, MRMR filters and GA wrapper approaches for gene selection. J. Theor. Appl. Inf. Technol. 46 (2012) 1034–1039. [Google Scholar]
  • A. Statnikov, C. Aliferis and I. Tsamardinos, Gems: Gene Expression Model Selector. Available at: http://www.gems-system.org (2005). [Google Scholar]
  • S. Tabakhi, A. Najafi, R. Ranjbar and P. Moradi, Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing 168 (2015) 1024–1036. [Google Scholar]
  • Z. Wang, Neuro-fuzzy modeling for microarray cancer gene expression data. First year transfer report. University of Oxford (2005). [Google Scholar]
  • S. Wang, W. Kong, W. Zeng and X. Hong, Hybrid binary imperialist competition algorithm and tabu search approach for feature selection using gene expression data. BioMed Res. Int. 2016 (2016) 9721713. [Google Scholar]
  • X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, et al., Top 10 algorithms in data mining. Knowl. Info. Syst. 14 (2008) 1–37. [CrossRef] [Google Scholar]
  • G.X. Yuan, C.H. Ho and C.J. Lin, Recent advances of large-scale linear classification. Proc. IEEE 100 (2012) 2584–2603. [CrossRef] [Google Scholar]
  • H. Yu, G. Gu, H. Liu, J. Shen and J. Zhao, A modified ant colony optimization algorithm for tumor marker gene selection. Genomics Proteomics Bioinf. 7 (2009) 200–208. [CrossRef] [Google Scholar]
  • W. Zhao, G. Wang, H.B. Wang, H.L. Chen, H. Dong and Z.D. Zhao, A novel framework for gene selection. Int. J. Adv. Comput. Technol. 3 (2011) 184–191. [Google Scholar]
  • A. Zibakhsh and M.S. Abadeh, Gene selection for cancer tumor detection using a novel memetic algorithm with a multi-view fitness function. Eng. App. Artif. Intell. 26 (2013) 1274–1281. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.