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[1] C. Enders, Applied missing data analysis. Guilford Press, New York, 2010.Search in Google Scholar

[2] J. Osborne, Best Practices in Data Cleaning. SAGE, 2013.Search in Google Scholar

[3] P. Schmitt, J. Mandel, M. Guedj, A Comparison of Six Methods for Missing Data Imputation. Journal of Biometrics & Biostatistics, 6(1), 2015, 1-6.Search in Google Scholar

[4] G. Ridgeway, Generalized Boosted Models: A guide to the gbm package. Update 1.1, 2007. www.saedsayad.com/docs/gbm2.pdf. Accessed 20 October 2016.Search in Google Scholar

[5] M. Richards, Fundamentals of radar signal processing. Tata McGraw-Hill Education, 2005.Search in Google Scholar

[6] I. Jordanov, N. Petrov, Intelligent Radar Signal Recognition and Classification. In Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds.) Recent Advances in Computational Intelligence in Defense and Security, 2016, 101-135.10.1007/978-3-319-26450-9_5Search in Google Scholar

[7] I. Jordanov, N. Petrov, A. Petrozziello, Supervised radar signal classification. Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE., 2016, 1464-1471.10.1109/IJCNN.2016.7727371Search in Google Scholar

[8] L. Carro-Calvo, et al., An evolutionary multiclass algorithm for automatic classification of high range resolution radar targets. Integrated Computer-Aided Engineering, 16(1), 2009, 51-60.10.3233/ICA-2009-0303Search in Google Scholar

[9] E. Granger, M. Rubin, S. Grossberg, P. Lavoie, A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks, 14 (3), 2001, 325-344.10.1016/S0893-6080(01)00019-3Open DOISearch in Google Scholar

[10] S. Maytal, F. Provost, Handling missing values when applying classification models. Journal of Machine Learning Research, 8, 2007, 1625-1657.Search in Google Scholar

[11] N. Ibrahim, R. Abdullah, M. Saripan, Artificial neural network approach in radar target classification. Journal of Computer Science, 5(1), 2009, 23.10.3844/jcssp.2009.23.32Search in Google Scholar

[12] M. Ahmadlou, H. Adeli, Enhanced probabilistic neural network with local decision circles: A robust classifier. Integrated Computer-Aided Engineering, 17(3), 2010, 197-210.10.3233/ICA-2010-0345Search in Google Scholar

[13] Z. Yin, W. Yang, Z. Yang, L. Zuo, H. Gao, A study on radar emitter recognition based on SPDS neural network. Information Technology Journal, 10(4), 2011, 883-888.10.3923/itj.2011.883.888Search in Google Scholar

[14] M. Gong, J. Zhao, J. Liu, Q. Miao, L. Jiao, Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks, IEEE Trans. on Neural Networks and Learning Systems, 27(1), 2016, 125-138.10.1109/TNNLS.2015.243578326068879Search in Google Scholar

[15] C. Shieh, C. Lin, A vector neural network for emitter identification. IEEE Trans. on Antennas and Propagation, 50(8), 2002, 1120-1127.10.1109/TAP.2002.801387Search in Google Scholar

[16] S. Zhai, T. Jiang, A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine, Neurocomputing, 149(1), 2015, 573-584.10.1016/j.neucom.2014.08.017Search in Google Scholar

[17] Z. Xin, W. Ying, Y. Bin, Signal classification method based on support vector machine and high-order cumulants. Wireless Sensor Network, 2(1), 2010, 48-52.10.4236/wsn.2010.21007Search in Google Scholar

[18] E. Abdulkadir, I. Onaran, Pulse Doppler radar target recognition using a two-stage SVM procedure. Aerospace and Electronic Systems, 47(2), 2011, 1450-1457.10.1109/TAES.2011.5751269Search in Google Scholar

[19] A. Karatzoglou, M. David, H. Kurt, Support vector machines in R, Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005.10.18637/jss.v015.i09Search in Google Scholar

[20] L. Breiman, Random forests. Machine Learning, 45(1), 2001, 5-32.10.1023/A:1010933404324Open DOISearch in Google Scholar

[21] A. Yali, D. Geman, Shape quantization and recognition with randomized trees. Neural computation, 9(7), 1997, 1545-1588.10.1162/neco.1997.9.7.1545Search in Google Scholar

[22] M. Fernandez-Delgado, E. Cernadas, S. Barro, D. Amorim, Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 2014, 3133-3181.Search in Google Scholar

[23] M. Wainberg, B. Alipanahi, B. Frey, Are Random Forests Truly the Best Classifiers? Journal of Machine Learning Research 17, 2016, 1-5.10.1186/s12864-016-3121-4505565927717327Search in Google Scholar

[24] I. Jordanov, N. Petrov, Sets with Incomplete and Missing Data – NN Radar Signal Classification. IEEE WCCI’14 World Congress on Computational Intelligence, Beijing, China, 2014, 218-225.10.1109/IJCNN.2014.6889852Search in Google Scholar

[25] R. Geaur, Z. Islam, A decision tree-based missing value imputation technique for data pre-processing. Proceedings of the Ninth Australasian Data Mining Conference, 121, 2011, 41-50.Search in Google Scholar

[26] A. Feelders, Handling missing data in trees surrogate splits or statistical imputation? Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2009, 329-334.10.1007/978-3-540-48247-5_38Search in Google Scholar

[27] A. Petrozziello, I. Jordanov, Data Analytics for Online Travelling Recommendation System: A Case Study. Proceedings of the IASTED International Conference Modelling, Identification and Control (MIC 2017), Innsbruck, Austria, 2017, 106-112.10.2316/P.2017.848-041Search in Google Scholar

[28] M. Templ, A. Kowarik, P. Filzmoser, Iterative stepwise regression imputation using standard and robust methods. Journal of Computational Statistics and Data Analysis, 55, 2011, 2793-2806.10.1016/j.csda.2011.04.012Open DOISearch in Google Scholar

[29] S. Verboven, K. Branden, P. Goos, Sequential imputation for missing values. Computational Biology and Chemistry, 31(5), 2007, 320-327.10.1016/j.compbiolchem.2007.07.00117920334Open DOISearch in Google Scholar

[30] F. Sarro, A. Petrozziello, M. Harman, Multi-objective software effort estimation. Proceedings of the 38th International Conference on Software Engineering, ACM, 2016, 619-630).10.1145/2884781.2884830Search in Google Scholar

[31] J. Cohen, Statistical power analysis for the behavioural sciences. Routledge, New York, 2013.10.4324/9780203771587Search in Google Scholar

[32] P. Dalgaard, Introductory Statistics with R. Springer, New York, 2008.10.1007/978-0-387-79054-1Search in Google Scholar

[33] J. Huang, C. Ling, Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3), 2005, 299-310.10.1109/TKDE.2005.50Open DOISearch in Google Scholar

[34] D. Hand, R. Till, A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine learning, 45(2), 2001, 171-186.10.1023/A:1010920819831Search in Google Scholar

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