A feature selection method with feature ranking using genetic programming

References

• Agrawal, P., Ganesh, T., & Mohamed, A. W. (2021a). Chaotic gaining sharing knowledge-based optimization algorithm: An improved metaheuristic algorithm for feature selection. Soft Computing, 25(14), 9505–9528. https://doi.org/10.1007/s00500-021-05874-3
   Google Scholar
• Agrawal, P., Ganesh, T., & Mohamed, A. W. (2021b). A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection. Neural Computing and Applications, 33(11), 5989–6008. https://doi.org/10.1007/s00521-020-05375-8
   Web of Science ®Google Scholar
• Agrawal, P., Ganesh, T., Oliva, D., & Mohamed, A. W. (2022). S-shaped and V-shaped gaining-sharing knowledge-based algorithm for feature selection. Applied Intelligence, 52 (1), 81–112. https://doi.org/10.1007/s10489-021-02233-5
   Google Scholar
• Ahmed, S., Zhang, M., & Peng, L. (2014). Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming. Connection Science, 26(3), 215–243. https://doi.org/10.1080/09540091.2014.906388
   Web of Science ®Google Scholar
• Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., & Zhang, M. (2019). A survey on evolutionary machine learning. Journal of the Royal Society of New Zealand, 49(2), 205–228. https://doi.org/10.1080/03036758.2019.1609052
   Web of Science ®Google Scholar
• Bi, Y., Xue, B., & Zhang, M. (2021a). Genetic programming for image classification: An automated approach to feature learning. Springer Nature.
   Google Scholar
• Bi, Y., Xue, B., & Zhang, M. (2021b). Multi-objective genetic programming for feature learning in face recognition. Applied Soft Computing, 103(4), 107152. https://doi.org/10.1016/j.asoc.2021.107152
   Google Scholar
• Canuto, A. M. P., & Nascimento, D. S. C. (2012). A genetic-based approach to features selection for ensembles using a hybrid and adaptive fitness function. The 2012 International Joint Conference on Neural Networks (IJCNN), (pp.1-8), Brisbane, QLD, Australia, June 2012.
   Google Scholar
• Davis, R. A., Charlton, A. J., Oehlschlager, S., & Wilson, J. C. (2006). Novel feature selection method for genetic programming using metabolomic 1H NMR data. Chemometrics and Intelligent Laboratory Systems, 81(1), 50–59. https://doi.org/10.1016/j.chemolab.2005.09.006
   Web of Science ®Google Scholar
• Dheeru, D., & Karra Taniskidou, E. (2017). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences
   Google Scholar
• Espejo, P. G., Ventura, S., & Herrera, F. (2010). A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(2), 121–144. https://doi.org/10.1109/TSMCC.2009.2033566
   Web of Science ®Google Scholar
• Friedlander, A., Neshatian, K., & Zhang, M. (2011). Meta-learning and feature ranking using genetic programming for classification: Variable terminal weighting. IEEE Congress on Evolutionary Computation, (pp. 5–8), New Orleans, LA, USA, June 2011.
   Google Scholar
• Gutlein, M., Frank, E., Hall, M., & Karwath, A. (2009). Large-scale attribute selection using wrappers. IEEE symposium on computational intelligence and data mining, (pp. 332–339), Nashville, TN, USA, May 2009.
   Google Scholar
• Hall, M. (1999). Correlation-based feature selection for machine learning (Unpublished doctoral dissertation). The University of Waikato.
   Google Scholar
• Hall, M., & Smith, L. (1999). Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper. Twelfth International Florida Artificial Intelligence Research Society Conference, (pp. 235–239), Orlando, Florida, USA, May 1999.
   Google Scholar
• Hancer, E. (2019). Fuzzy kernel feature selection with multi-objective differential evolution algorithm. Connection Science, 31(4), 323–341. https://doi.org/10.1080/09540091.2019.1639624
   Web of Science ®Google Scholar
• Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods (2nd ed.). Wiley-Interscience.
   Google Scholar
• Hunt, R., Neshatian, K., & Zhang, M. (2012). A genetic programming approach to hyper-heuristic feature selection. The 9th international conference on Simulated Evolution and Learning, (pp. 320–330), Berlin, Heidelberg, December 2012.
   Google Scholar
• Kira, K., & Rendell, L. A. (1992). The feature selection problem: Traditional methods and a new algorithm. The tenth national conference on artificial intelligence, (pp. 129–134), San Jose California, July 1992.
   Google Scholar
• Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. MIT Press.
   Google Scholar
• Koza, J. R., Andre, D., Bennett, F. H., & Keane, M. A. (1999). Genetic programming III: Darwinian invention and problem solving. IEEE Transactions on Evolutionary Computation, 7(4), 451–453. https://doi.org/10.1162/evco.1999.7.4.451
   Google Scholar
• Liang, Y., Zhang, M., & Browne, W. N. (2017). Image feature selection using genetic programming for figure-ground segmentation. Engineering Applications of Artificial Intelligence, 62(12), 96–108. https://doi.org/10.1016/j.engappai.2017.03.009
   Google Scholar
• Lin, J. Y., Ke, H. R., Chien, B. C., & Yang, W. P. (2008). Classifier design with feature selection and feature extraction using layered genetic programming. Expert Systems with Applications, 34(2), 1384–1393. https://doi.org/10.1016/j.eswa.2007.01.006
   Web of Science ®Google Scholar
• Loughran, R., Agapitos, A., Kattan, A., Brabazon, A., & O'Neill, M. (2017). Feature selection for speaker verification using genetic programming. Evolutionary Intelligence, 10(1–2), 1–21. https://doi.org/10.1007/s12065-016-0150-5
   Web of Science ®Google Scholar
• Luke, S.. (2017). ECJ then and now. Genetic and Evolutionary Computation Conference Companion Pages, (pp.1223–1230), Berlin Germany, July 2017.
   Google Scholar
• Ma, J., & Gao, X. (2020a). Designing genetic programming classifiers with feature selection and feature construction. Applied Soft Computing, 97(4), 106826. https://doi.org/10.1016/j.asoc.2020.106826
   Google Scholar
• Ma, J., & Gao, X. (2020b). A filter-based feature construction and feature selection approach for classification using genetic programming. Knowledge-Based Systems, 196(6), 105806. https://doi.org/10.1016/j.knosys.2020.105806
   Google Scholar
• Ma, J., & Teng, G. (2019). A hybrid multiple feature construction approach using genetic programming. Applied Soft Computing, 80(2), 687–699. https://doi.org/10.1016/j.asoc.2019.04.039
   Google Scholar
• Mei, Y., Nguyen, S., Xue, B., & Zhang, M. (2017). An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(5), 339–353. https://doi.org/10.1109/TETCI.2017.2743758
   Google Scholar
• Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2020). Gaining-sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 11(7), 1501–1529. https://doi.org/10.1007/s13042-019-01053-x
   Web of Science ®Google Scholar
• Muni, D. P., Pal, N. R., & Das, J. (2006). Genetic programming for simultaneous feature selection and classifier design. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(1), 106–117. https://doi.org/10.1109/TSMCB.2005.854499
   PubMed Web of Science ®Google Scholar
• Nagra, A. A., Han, F., Ling, Q. H., Abubaker, M., Ahmad, F., Mehta, S., & Apasiba, A. T. (2020). Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems. Connection Science, 32(1), 16–36. https://doi.org/10.1080/09540091.2019.1609419
   Web of Science ®Google Scholar
• Neshatian, K. (2010). Feature manipulation with genetic programming (Unpublished doctoral dissertation). Victoria University of Wellington.
   Google Scholar
• Neshatian, K., & Zhang, M. (2009a). Genetic programming for feature subset ranking in binary classification problems. The 12th European Conference on Genetic Programming, (pp. 121–132), Berlin, Heidelberg, April 2009.
   Google Scholar
• Neshatian, K., & Zhang, M. (2009b). Pareto front feature selection: Using genetic programming to explore feature space. The 11th annual genetic and evolutionary computation conference, (pp. 1027–1034), Montreal, Québec, Canada, July 2009.
   Google Scholar
• Neshatian, K., & Zhang, M. (2009c). Unsupervised elimination of redundant features using genetic programming. Australasian joint conference on artificial intelligence, (pp. 432–442), Berlin, Heidelberg, November 2009.
   Google Scholar
• Neshatian, K., & Zhang, M. (2011). Using genetic programming for context-sensitive feature scoring in classification problems. Connection Science, 23(3), 183–207. https://doi.org/10.1080/09540091.2011.630065
   Web of Science ®Google Scholar
• Neshatian, K., & Zhang, M. (2012). Improving relevance measures using genetic programming. European conference on genetic programming, (pp. 97–108), Berlin, Heidelberg, April 2012.
   Google Scholar
• Papa, J. P., Rosa, G. H., & Papa, L. P. (2017). A binary-constrained geometric semantic genetic programming for feature selection purposes. Pattern Recognition Letters, 100(24), 59–66. https://doi.org/10.1016/j.patrec.2017.10.002
   Google Scholar
• Patel, S. P., & Upadhyay, S. H. (2020). Euclidean distance based feature ranking and subset selection for bearing fault diagnosis. Expert Systems with Applications, 154(8), 113400. https://doi.org/10.1016/j.eswa.2020.113400
   Google Scholar
• Peng, B., Wan, S., Bi, Y., Xue, B., & Zhang, M. (2020). Automatic feature extraction and construction using genetic programming for rotating machinery fault diagnosis. IEEE Transactions on Cybernetics, (99), 1–15. https://doi.org/10.1109/TCYB.2020.3032945
   Google Scholar
• Purohit, A., Chaudhari, N., & Tiwari, A. (2010). Construction of classifier with feature selection based on genetic programming. In Proceedings of the IEEE congress on evolutionary computation.
   Google Scholar
• Ribeiro, I., Andrade, G., Viegas, F., Madeira, D., Rocha, L., Salles, T., & Gonçalves, M. (2012). Aggressive and effective feature selection using genetic programming. In Proceedings of IEEE congress on evolutionary computation.
   Google Scholar
• Sreeja, N. K. (2019). A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection. Connection Science, 31(2), 143–168. https://doi.org/10.1080/09540091.2018.1512558
   Web of Science ®Google Scholar
• Tan, F., Fu, X., Zhang, Y., & Bourgeois, A. G. (2008). A genetic algorithm-based method for feature subset selection. Soft Computing, 12(2), 111–120. https://doi.org/10.1007/s00500-007-0193-8
   Web of Science ®Google Scholar
• Too, J., & Abdullah, A. R. (2020). Binary atom search optimisation approaches for feature selection. Connection Science, 32(4), 406–430. https://doi.org/10.1080/09540091.2020.1741515
   Web of Science ®Google Scholar
• Too, J., Sadiq, A. S., & Mirjalili, S. M. (2021). A conditional opposition-based particle swarm optimisation for feature selection. Connection Science, 77(3), 1–23. https://doi.org/10.1080/09540091.2021.2002266
   Google Scholar
• 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. https://doi.org/10.1007/s00521-013-1368-0
   Web of Science ®Google Scholar
• Viegas, F., Rocha, L., Gonçalves, M., Mourão, F., Sá, G., Salles, T., & Sandin, I. (2018). A genetic programming approach for feature selection in highly dimensional skewed data. Neurocomputing, 273(3), 554–569. https://doi.org/10.1016/j.neucom.2017.08.050
   Google Scholar
• Xue, B., Zhang, M., & Browne, W. (2013). Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics, 43(6), 1656–1671. https://doi.org/10.1109/TSMCB.2012.2227469
   PubMed Web of Science ®Google Scholar
• Xue, B., Zhang, M., & Browne, W. N. (2014). Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied Soft Computing, 18(C), 261–276. https://doi.org/10.1016/j.asoc.2013.09.018
   Google Scholar