How to cite this paper
Nouri-Moghaddam, B., Ghazanfari, M & Fathian, M. (2020). A novel filter-wrapper hybrid gene selection approach for microarray data based on multi-objective forest optimization algorithm.Decision Science Letters , 9(3), 271-290.
Refrences
Almugren, N., & Alshamlan, H. (2019). A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification. IEEE Access, 7, 78533–78548.
Amaldi, E., & Kann, V. (1998). On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theoretical Computer Science, 209(1–2), 237–260.
Annavarapu, C. S. R., Dara, S., & Banka, H. (2016). Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm. EXCLI Journal, 15, 460–473.
Apolloni, J., Leguizamón, G., & Alba, E. (2016). Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Applied Soft Computing, 38, 922–932.
Auger, A., Bader, J., Brockhoff, D., & Zitzler, E. (2009). Theory of the hypervolume indicator: Optimal μ-distributions and the choice of the reference point. In Proceedings of the 10th ACM SIGEVO Workshop on Foundations of Genetic Algorithms, FOGA’09 (pp. 87–102). ACM Press.
Baliarsingh, S. K., Vipsita, S., Muhammad, K., & Bakshi, S. (2019). Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm and Evolutionary Computation, 48(May), 262–273.
Baliarsingh, S. K., Vipsita, S., Muhammad, K., Dash, B., & Bakshi, S. (2019). Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm. Applied Soft Computing Journal, 77, 520–532.
Banerjee, M., Mitra, S., & Banka, H. (2007). Evolutionary rough feature selection in gene expression data. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 37(4), 622–632.
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Benítez, J. M., & Herrera, F. (2014). A review of microarray datasets and applied feature selection methods. Information Sciences, 282, 111–135.
Brockhoff, D., Friedrich, T., & Neumann, F. (2008). Analyzing hypervolume indicator based algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5199 LNCS, pp. 651–660).
Chakraborty, G., & Chakraborty, B. (2013). Multi-objective optimization using pareto GA for gene-selection from microarray data for disease classification. Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2629–2634.
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers and Electrical Engineering, 40(1), 16–28.
Chuang, L. Y., Yang, C. H., & Yang, C. H. (2009). Tabu search and binary particle swarm optimization for feature selection using microarray data. Journal of Computational Biology, 16(12), 1689–1703.
Chuang, L. Y., Yang, C. H., Wu, K. C., & Yang, C. H. (2011). A hybrid feature selection method for DNA microarray data. Computers in Biology and Medicine, 41(4), 228–237.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (Vol. 5). Boston, MA: Springer US.
Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600) (Vol. 2, pp. 1051–1056). IEEE.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Dashtban, M., & Balafar, M. (2017). Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics, 109(2), 91–107.
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.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons. Retrieved from https://www.wiley.com/en-us/Multi+Objective+Optimization+using+Evolutionary+Algorithms-p-9780471873396
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Gangavarapu, T., & Patil, N. (2019). A novel filter–wrapper hybrid greedy ensemble approach optimized using the genetic algorithm to reduce the dimensionality of high-dimensional biomedical datasets. Applied Soft Computing Journal, 81, 105538.
Ghaemi, M., & Feizi-Derakhshi, M.-R. (2014). Forest Optimization Algorithm. Expert Systems with Applications, 41(15), 6676–6687.
Ghaemi, M., & Feizi-Derakhshi, M.-R. (2016). Feature selection using Forest Optimization Algorithm. Pattern Recognition, 60, 121–129.
Gu, Q., Li, Z., & Han, J. (2012). Generalized Fisher Score for Feature Selection. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, 266–273. Retrieved from http://arxiv.org/abs/1202.3725
Hall, M. A. (1999). Correlation-based Feature Selection for Machine Learning. The University of Waikato.
Hancer, E., Xue, B., Zhang, M., Karaboga, D., & Akay, B. (2018). Pareto front feature selection based on artificial bee colony optimization. Information Sciences, 422, 462–479.
Hasnat, A., & Molla, A. U. (2017). Feature selection in cancer microarray data using multi-objective genetic algorithm combined with correlation coefficient. Proceedings of IEEE International Conference on Emerging Technological Trends in Computing, Communications and Electrical Engineering, ICETT 2016.
He, X., Cai, D., & Niyogi, P. (2005). Laplacian Score for feature selection. Advances in Neural Information Processing Systems, 507–514.
Jensen, M. T. (2003). Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. IEEE Transactions on Evolutionary Computation, 7(5), 503–515.
Jonnalagadda, S., & Srinivasan, R. (2008). Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data. BMC Bioinformatics, 9(1), 267.
Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. Proceedings of 2014 Science and Information Conference, SAI 2014, (August 2014), 372–378.
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324.
Lai, C. M. (2018). Multi-objective simplified swarm optimization with weighting scheme for gene selection. Applied Soft Computing Journal, 65, 58–68.
Lee, C. P., & Leu, Y. (2011). A novel hybrid feature selection method for microarray data analysis. Applied Soft Computing Journal, 11(1), 208–213.
Li, X., & Yin, M. (2013). Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Transactions on Nanobioscience, 12(4), 343–353.
Lu, H., Chen, J., Yan, K., Jin, Q., Xue, Y., & Gao, Z. (2017). A hybrid feature selection algorithm for gene expression data classification. Neurocomputing, 256, 56–62.
Miao, J., & Niu, L. (2016). A Survey on Feature Selection. Procedia Computer Science, 91(Itqm), 919–926.
Mishra, S., Shaw, K., & Mishra, D. (2012). A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data. Procedia Technology, 4, 802–806.
Mohamad, M. S., Omatu, S., Deris, S., & Yoshioka, M. (2008). Multi-objective optimization using genetic algorithm for gene selection from microarray data. Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development, 1331–1334.
Mohapatra, S., Aryendu, I., Panda, A., & Padhi, A. K. (2018). A Modern Approach for Load Balancing Using Forest Optimization Algorithm. In 2018 Second International Conference on Computing Methodologies and Communication (ICCMC) (pp. 85–90). IEEE.
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 in Medicine Unlocked, 9(August), 246–254.
Mukhopadhyay, A., Member, S., Maulik, U., & Member, S. (2014). A Survey of Multiobjective Evolutionary Algorithms for Data Mining : Part I, 18(1), 4–19.
Nguyen, B. H., Xue, B., & Zhang, M. (2020). A survey on swarm intelligence approaches to feature selection in data mining. Swarm and Evolutionary Computation, 54(February), 100663.
Ratnoo, S., & Ahuja, J. (2017). Dimension reduction for microarray data using multi-objective ant colony optimisation. International Journal of Computational Systems Engineering, 3(1/2), 58.
Remeseiro, B., & Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Computers in Biology and Medicine, 112(February), 103375.
Shahbeig, S., Rahideh, A., Helfroush, M. S., & Kazemi, K. (2018). Gene selection from large-scale gene expression data based on fuzzy interactive multi-objective binary optimization for medical diagnosis. Biocybernetics and Biomedical Engineering, 38(2), 313–328.
Sharma, A., & Rani, R. (2019). C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods. Computer Methods and Programs in Biomedicine, 178, 219–235.
Shen, Q., Shi, W. M., & Kong, W. (2008). Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Computational Biology and Chemistry, 32(1), 53–60.
Shukla, A. K., Singh, P., & Vardhan, M. (2018). A hybrid gene selection method for microarray recognition. Biocybernetics and Biomedical Engineering, 38(4), 975–991.
Shukla, A. K., Singh, P., & Vardhan, M. (2020). Gene selection for cancer types classification using novel hybrid metaheuristics approach. Swarm and Evolutionary Computation, 54(December 2019).
Sierra, M. R., & Coello Coello, C. A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. In International conference on evolutionary multi-criterion optimization (pp. 505–519). Springer.
Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. Wiley.
Tyagi, V., & Mishra, A. (2013). A Survey on Different Feature Selection Methods for Microarray Data Analysis. International Journal of Computer Applications, 67(16), 36–40.
Vafaee Sharbaf, F., Mosafer, S., & Moattar, M. H. (2016). A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics, 107(6), 231–238.
Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175–186.
Yang, C. H., Chuang, L. Y., & Yang, C. H. (2010). IG-GA: A hybrid filter/wrapper method for feature selection of microarray data. Journal of Medical and Biological Engineering, 30(1), 23–28.
Zhu, Z., Ong, Y. S., & Dash, M. (2007). Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognition, 40(11), 3236–3248.
Amaldi, E., & Kann, V. (1998). On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theoretical Computer Science, 209(1–2), 237–260.
Annavarapu, C. S. R., Dara, S., & Banka, H. (2016). Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm. EXCLI Journal, 15, 460–473.
Apolloni, J., Leguizamón, G., & Alba, E. (2016). Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Applied Soft Computing, 38, 922–932.
Auger, A., Bader, J., Brockhoff, D., & Zitzler, E. (2009). Theory of the hypervolume indicator: Optimal μ-distributions and the choice of the reference point. In Proceedings of the 10th ACM SIGEVO Workshop on Foundations of Genetic Algorithms, FOGA’09 (pp. 87–102). ACM Press.
Baliarsingh, S. K., Vipsita, S., Muhammad, K., & Bakshi, S. (2019). Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm and Evolutionary Computation, 48(May), 262–273.
Baliarsingh, S. K., Vipsita, S., Muhammad, K., Dash, B., & Bakshi, S. (2019). Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm. Applied Soft Computing Journal, 77, 520–532.
Banerjee, M., Mitra, S., & Banka, H. (2007). Evolutionary rough feature selection in gene expression data. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 37(4), 622–632.
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Benítez, J. M., & Herrera, F. (2014). A review of microarray datasets and applied feature selection methods. Information Sciences, 282, 111–135.
Brockhoff, D., Friedrich, T., & Neumann, F. (2008). Analyzing hypervolume indicator based algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5199 LNCS, pp. 651–660).
Chakraborty, G., & Chakraborty, B. (2013). Multi-objective optimization using pareto GA for gene-selection from microarray data for disease classification. Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2629–2634.
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers and Electrical Engineering, 40(1), 16–28.
Chuang, L. Y., Yang, C. H., & Yang, C. H. (2009). Tabu search and binary particle swarm optimization for feature selection using microarray data. Journal of Computational Biology, 16(12), 1689–1703.
Chuang, L. Y., Yang, C. H., Wu, K. C., & Yang, C. H. (2011). A hybrid feature selection method for DNA microarray data. Computers in Biology and Medicine, 41(4), 228–237.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (Vol. 5). Boston, MA: Springer US.
Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600) (Vol. 2, pp. 1051–1056). IEEE.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Dashtban, M., & Balafar, M. (2017). Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics, 109(2), 91–107.
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.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons. Retrieved from https://www.wiley.com/en-us/Multi+Objective+Optimization+using+Evolutionary+Algorithms-p-9780471873396
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Gangavarapu, T., & Patil, N. (2019). A novel filter–wrapper hybrid greedy ensemble approach optimized using the genetic algorithm to reduce the dimensionality of high-dimensional biomedical datasets. Applied Soft Computing Journal, 81, 105538.
Ghaemi, M., & Feizi-Derakhshi, M.-R. (2014). Forest Optimization Algorithm. Expert Systems with Applications, 41(15), 6676–6687.
Ghaemi, M., & Feizi-Derakhshi, M.-R. (2016). Feature selection using Forest Optimization Algorithm. Pattern Recognition, 60, 121–129.
Gu, Q., Li, Z., & Han, J. (2012). Generalized Fisher Score for Feature Selection. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, 266–273. Retrieved from http://arxiv.org/abs/1202.3725
Hall, M. A. (1999). Correlation-based Feature Selection for Machine Learning. The University of Waikato.
Hancer, E., Xue, B., Zhang, M., Karaboga, D., & Akay, B. (2018). Pareto front feature selection based on artificial bee colony optimization. Information Sciences, 422, 462–479.
Hasnat, A., & Molla, A. U. (2017). Feature selection in cancer microarray data using multi-objective genetic algorithm combined with correlation coefficient. Proceedings of IEEE International Conference on Emerging Technological Trends in Computing, Communications and Electrical Engineering, ICETT 2016.
He, X., Cai, D., & Niyogi, P. (2005). Laplacian Score for feature selection. Advances in Neural Information Processing Systems, 507–514.
Jensen, M. T. (2003). Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. IEEE Transactions on Evolutionary Computation, 7(5), 503–515.
Jonnalagadda, S., & Srinivasan, R. (2008). Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data. BMC Bioinformatics, 9(1), 267.
Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. Proceedings of 2014 Science and Information Conference, SAI 2014, (August 2014), 372–378.
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324.
Lai, C. M. (2018). Multi-objective simplified swarm optimization with weighting scheme for gene selection. Applied Soft Computing Journal, 65, 58–68.
Lee, C. P., & Leu, Y. (2011). A novel hybrid feature selection method for microarray data analysis. Applied Soft Computing Journal, 11(1), 208–213.
Li, X., & Yin, M. (2013). Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Transactions on Nanobioscience, 12(4), 343–353.
Lu, H., Chen, J., Yan, K., Jin, Q., Xue, Y., & Gao, Z. (2017). A hybrid feature selection algorithm for gene expression data classification. Neurocomputing, 256, 56–62.
Miao, J., & Niu, L. (2016). A Survey on Feature Selection. Procedia Computer Science, 91(Itqm), 919–926.
Mishra, S., Shaw, K., & Mishra, D. (2012). A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data. Procedia Technology, 4, 802–806.
Mohamad, M. S., Omatu, S., Deris, S., & Yoshioka, M. (2008). Multi-objective optimization using genetic algorithm for gene selection from microarray data. Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development, 1331–1334.
Mohapatra, S., Aryendu, I., Panda, A., & Padhi, A. K. (2018). A Modern Approach for Load Balancing Using Forest Optimization Algorithm. In 2018 Second International Conference on Computing Methodologies and Communication (ICCMC) (pp. 85–90). IEEE.
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 in Medicine Unlocked, 9(August), 246–254.
Mukhopadhyay, A., Member, S., Maulik, U., & Member, S. (2014). A Survey of Multiobjective Evolutionary Algorithms for Data Mining : Part I, 18(1), 4–19.
Nguyen, B. H., Xue, B., & Zhang, M. (2020). A survey on swarm intelligence approaches to feature selection in data mining. Swarm and Evolutionary Computation, 54(February), 100663.
Ratnoo, S., & Ahuja, J. (2017). Dimension reduction for microarray data using multi-objective ant colony optimisation. International Journal of Computational Systems Engineering, 3(1/2), 58.
Remeseiro, B., & Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Computers in Biology and Medicine, 112(February), 103375.
Shahbeig, S., Rahideh, A., Helfroush, M. S., & Kazemi, K. (2018). Gene selection from large-scale gene expression data based on fuzzy interactive multi-objective binary optimization for medical diagnosis. Biocybernetics and Biomedical Engineering, 38(2), 313–328.
Sharma, A., & Rani, R. (2019). C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods. Computer Methods and Programs in Biomedicine, 178, 219–235.
Shen, Q., Shi, W. M., & Kong, W. (2008). Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Computational Biology and Chemistry, 32(1), 53–60.
Shukla, A. K., Singh, P., & Vardhan, M. (2018). A hybrid gene selection method for microarray recognition. Biocybernetics and Biomedical Engineering, 38(4), 975–991.
Shukla, A. K., Singh, P., & Vardhan, M. (2020). Gene selection for cancer types classification using novel hybrid metaheuristics approach. Swarm and Evolutionary Computation, 54(December 2019).
Sierra, M. R., & Coello Coello, C. A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. In International conference on evolutionary multi-criterion optimization (pp. 505–519). Springer.
Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. Wiley.
Tyagi, V., & Mishra, A. (2013). A Survey on Different Feature Selection Methods for Microarray Data Analysis. International Journal of Computer Applications, 67(16), 36–40.
Vafaee Sharbaf, F., Mosafer, S., & Moattar, M. H. (2016). A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics, 107(6), 231–238.
Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175–186.
Yang, C. H., Chuang, L. Y., & Yang, C. H. (2010). IG-GA: A hybrid filter/wrapper method for feature selection of microarray data. Journal of Medical and Biological Engineering, 30(1), 23–28.
Zhu, Z., Ong, Y. S., & Dash, M. (2007). Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognition, 40(11), 3236–3248.