Acknowledgement
This work was supported by the Hyupsung University Research Grant of 2020.
References
- Yun Xu and Royston Goodacre. On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. Journal of Analysis and Testing. 2:249-262. (2018) https://doi.org/10.1007/s41664-018-0068-2
- Andrew Ng. Model Selection and Train/Validation/Test sets. Machine Learning @ Coursera.
- Shin Nakajima and Kai Ngoc BUI. Dataset Coverage for Testing Machine Learning Computer Programs. Proceedings of 23rd Asia- Pacific Software Engineering Conference; 2016 Dec 6-9; New Zealand: IEEE, (2016)
- Arnab Sharma and Keike Wehrheim. Testing Machine Learning Algorithms for Balanced Data Usage. Proceeding of 12th International Conference of Software Testing, Verification and Validation; 2019 April 22-27; China: IEEE; (2019)
- Senthil Mani and Anush Sankaran. Coverage Testing of Deep Learning Models using Dataset Characterization. ArXiv. 2019; arXiv:1911.07309.(2019)
- Du Zhang and Jeffrey Tsai. Machine Learning Applications in Software Engineering. World Scientific; (2005)
- F. Pedregosa et al. Scikit-learn: Machine Learning Systems with Python. Journal of Machine Learning Research. 12(85):2825-2830. (2011)
- D. Albanese, G. Merler, S.and Jurman, and R. Visintainer. MLPy: high-performance python package for predictive modelling. Proceeding on Workshop on Machine Learning Open Source Software. 2008 December 12; Canada: PASCAL. (2008)
- T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Ruckstiess, and J. Schmidhuber. PyBrain. The Journal of Machine Learning Research. 11:743-746. (2010)
- M. Hanke, Y.O. Halchenko, P.B. Sederberg, S.J. Hanson, J.V. Haxby, and S. Pollmann. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuro informatics. 7(1):37-53. (2009)
- T. Zito, N.Wilbert, L.Wiskott, and P. Berkes. Modular toolkit for data processing (MDP): A Python data processing framework. Frontiers in Neuro informatics. January (2008)
- S. Sonnenburg, G. Ratsch, S. Henschel, C.Widmer, J. Behr, A. Zien, F. de Bona, A. Binder, C. Gehl, and V. Franc. The SHOGUN machine learning toolbox. Journal of Machine Learning Research. 11:1799-1802 (2010)
- I Guyon, S. R. Gunn, A. Ben-Hur, and G. Dror. Result analysis of the NIPS 2003 feature selection challenge. Proceedings of the 17th International Conference on Neural Information Processing Systems. 2004 December; Vancouver, Canada: MIT press. (2004)
- Dua D, Graff C. UCI Machine Learning Repository. Available from: http://archive.ics.uci.edu/ml (2017)
- R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Human Genetics. 7(2):179-188 (1936)
- R. O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. John Wiley Sons: New York (1973)
- B. V. Dasarathy. Nosing around the neighbourhood: A new system structure and classification rule for recognition in partially exposed environments. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-2(1):67-71 (1980) https://doi.org/10.1109/TPAMI.1980.4766972
- G.W. Gates. The reduced nearest neighbor rule. IEEE Transactions on Information Theory. 18(3): 431-433 (1972) https://doi.org/10.1109/TIT.1972.1054809
- Tao, D.C., Tang, X.O., Li, X.L., Wu, X.D. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(7), 1088-1099 (2006) https://doi.org/10.1109/TPAMI.2006.134
- Giorgio, V., Marco, M., Francesca, R. Cancer recognition with bagged ensembles of support vector machines. Neuro computing. 2004; 56: 461-466 (2004)
- Hyunsoo, K., Peg, H., Haesun, P. Dimension Reduction in Text Classification with Support Vector Machines. Journal of Machine Learning Research. 6:37-53 (2005)
- Bellotti, T., Crook, J. Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications. 36:3302-3308 (2009) https://doi.org/10.1016/j.eswa.2008.01.005