Abstract
The quality of the search space is an important factor that influences the performance of any machine learning algorithm including its classification. The attributes that define the search space can be poorly understood or inadequate, thereby making it difficult to discover high quality knowledge and understanding. Feature construction (FC) and feature selection (FS) are two pre-processing steps that can be used to improve the feature space quality, by enhancing the classifier performance in terms of accuracy, complexity, speed and interpretability. While FS aims to choose a set of informative features for improving the performance, FC can enhance the classification performance by evolving new features out of the original ones. The evolved features are expected to have more predictive value than the originals that make them up. Over the past few decades, several evolutionary computation (EC) methods have been proposed in the area of FC. This paper gives an overview of the literature on EC for FC. Here, we focus mainly on filter, wrapper and embedded methods, in which the contributions of these different methods are identified. Furthermore, some open challenges and current issues are also discussed in order to identify promising areas for future research.
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References
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall Press, USA (2009)
Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1–2), 245–271 (1997)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and subset selection problem. In: Proceedings of 11th International Conference on Machine Learning, pp. 121–129 (1994)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publisher (1993)
Liu, H., Motada, H.: Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers, Norwell (1998)
Xue, B., Zhang, M.: Evolutionary computation for feature manipulation: key challenges and future directions. In: Evolutionary Computation (CEC), pp. 3061–3067 (2016)
Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)
Tran, B., Zhang, M., Xue, B.: Multiple feature construction in classification on high-dimensional data using GP. In: SSCI, pp. 1–8 (2016)
Dai, Y., Xue, B., Zhang, M.: New representations in PSO for feature construction in classification. In: European Conference on the Applications of Evolutionary Computation, pp. 476–488. Springer, Berlin (2014)
Nguyen, H.B., Xue, B., Andreae, P.: A hybrid GA-GP method for feature reduction in classification. In: Asia-Pacific Conference on Simulated Evolution and Learning, pp. 591–604. Springer, Cham (2017)
Fogel, D.B.: Introduction to Evolutionary Computation. Wiley, New York (2007)
Vafaie, H., De Jong, K.: Genetic algorithms as a tool for restructuring feature space representations. In: Proceedings of 7th International Conference of Tools with AI, pp. 8–11 (1995)
Ahmed, S., Zhang, M., Peng, L., Xue, B.: Multiple feature construction for effective biomarker identification and classification using genetic programming. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 249–256 (2014a)
Eiben, A.E., Smith, J.: From evolutionary computation to the evolution of things. Nature 521(7553), 476 (2015)
Sondhi, P.: Feature construction methods: a survey. sifaka. uiuc. edu. 69, 70–71 (2009)
Lillywhite, K., Lee, D.J., Tippetts, B., Archibald, J.: A feature construction method for general object recognition. Pattern Recogn. 46(12), 3300–3314 (2013)
Drozdz, K., Kwasnicka, H.: Feature set reduction by evolutionary selection and construction. In: KES International Symposium on Agent and Multi-agent Systems: Technologies and Applications (pp. 140–149). Springer, Berlin (2010)
Shafti, L.S., Pérez, E.: Evolutionary multi-feature construction for data reduction: a case study. Appl. Soft Comput. 9(4), 1296–1303 (2009)
Larsen, O., Freitas, A.A., Nievola, J.C.: Constructing X-of-N attributes with a genetic algorithm. In: GECCO Late Breaking Papers, pp. 316–322 (2002)
GarcÃa, D., González, A., Pérez, R.: A two-step approach of feature construction for a genetic learning algorithm. In: Fuzzy Systems (FUZZ), pp. 1255–1262 (2011)
GarcÃa, D., González, A., Pérez, R.: A feature construction approach for genetic iterative rule learning algorithm. J. Comput. Syst. Sci. 80(1), 101–117 (2014)
Alfred, R.: DARA: data summarisation with feature construction. In: Second Asia International Conference on Modeling & Simulation, pp. 830–835. AICMS 08 (2008)
Firpi, H., Goodman, E., Echauz, J.: On prediction of epileptic seizures by computing multiple genetic programming artificial features. In: European Conference on Genetic Programming, pp. 321–330. Springer, Berlin (2005)
Chen, Q., Zhang, M., Xue, B.: Genetic programming with embedded feature construction for high-dimensional symbolic regression. In: Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, Nov 2016, pp. 87–102. Proceedings (2017)
Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genet. Program Evol. Mach. 3(4), 329–343 (2002)
Yazdani, S., Shanbehzadeh, J., Hadavandi, E.: MBCGP-FE: a modified balanced cartesian genetic programming feature extractor. Knowl.-Based Syst. 135, 89–98 (2017)
Ahmed, S., Zhang, M., Peng, L.: A new GP-based wrapper feature construction approach to classification and biomarker identification. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2756–2763 (2014b)
Elola, A., Del Ser, J., Bilbao, M.N., Perfecto, C., Alexandre, E., Salcedo-Sanz, S.: Hybridizing cartesian genetic programming and harmony search for adaptive feature construction in supervised learning problems. Appl. Soft Comput. 52, 760–770 (2017)
Suganuma, M., Tsuchiya, D., Shirakawa, S., Nagao, T.: Hierarchical feature construction for image classification using genetic programming. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 001423–001428 (2016)
Tariq, H., Eldridge, E., Welch, I.: An efficient approach for feature construction of high-dimensional microarray data by random projections. PLoS ONE 13(4), e0196385 (2018)
Tran, C.T., Zhang, M., Andreae, P., Xue, B.: Genetic programming based feature construction for classification with incomplete data. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1033–1040 (2017)
Muharram, M., Smith, G.: Evolutionary constructive induction. IEEE Trans. Knowl. Data Eng. 17, 1518–1528 (2005)
Guo, H., Nandi, A.K.: Breast cancer diagnosis using genetic programming generated feature. Pattern Recogn. 39(5), 980–987 (2006)
Liu, Q., Qiao, M., Sung, A.H.: Distance metric learning and support vector machines for classification of mass spectrometry proteomics data. Int. J. Knowl. Eng. Soft Data Paradig. 1(3), 216–226 (2009)
Muharram, M.A., Smith, G.D.: Evolutionary feature construction using information gain and gini index. In: European Conference on Genetic Programming, pp. 379–388. Springer, Berlin (2004)
Neshatian, K., Zhang, M., Andreae, P.: A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Trans. Evol. Comput. 16(5), 645–661 (2012)
Liang, Y., Zhang, M., Browne, W.N.: Feature construction using genetic programming for figure-ground image segmentation. In: Intelligent and Evolutionary Systems, pp. 237–250. Springer, Cham (2017)
Tran, B., Xue, B., Zhang, M.: Genetic programming for feature construction and selection in classification on high-dimensional data. Memet. Comput. 8(1), 3–15 (2015)
Ahmed, S., Zhang, M., Peng, L., Xue, B.: A Multi-objective genetic programming biomarker detection approach in mass spectrometry data. In: European Conference on the Applications of Evolutionary Computation, pp. 106–122. Springer, Cham (2016)
Cano, A., Ventura, S., Cios, K.J.: Multi-objective genetic programming for feature extraction and data visualization. Soft Comput. 21(8), 2069–2089 (2017)
Xue, B., Zhang, M., Dai, Y., Browne, W.N.: PSO for feature construction and binary classification. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO, pp. 137–144 (2013)
Mahanipour, A., Nezamabadi-pour, H.: Improved PSO-based feature construction algorithm using feature selection methods. In: Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 1–5 (2017)
Tran, B., Xue, B., Zhang, M.: Using feature clustering for GP-based feature construction on high-dimensional data. In: European Conference on Genetic Programming, pp. 210–226. Springer, Cham (2017)
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This work is supported by University Kebangsaan Malaysia, under grant number FRGS/1/2016/ICT02/UKM/01/2.
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Swesi, I.M.A.O., Bakar, A.A. (2020). Recent Developments on Evolutionary Computation Techniques to Feature Construction. In: Huk, M., Maleszka, M., Szczerbicki, E. (eds) Intelligent Information and Database Systems: Recent Developments. ACIIDS 2019. Studies in Computational Intelligence, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-030-14132-5_9
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