Abstract
The raging of COVID-19 has been going on for a long time. Thus, it is essential to find a more accurate classification model for recognizing positive cases. In this paper, we use a variety of classification models to recognize the positive cases of SARS. We conduct evaluation with two types of SARS datasets, numerical and categorical types. For the sake of more clear interpretability, we also generate explanatory rules for the models. Our prediction models and rule generation models both get effective results on these two kinds of datasets. All explanatory rules achieve an accuracy of more than 70%, which indicates that the classification model can have strong inherent explanatory ability. We also make a brief analysis of the characteristics of different rule generation models. We hope to provide new possibilities for the interpretability of the classification models.
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References
Pedersen, S.F., Ho, Y.C.: SARS-CoV-2: a storm is raging. J. Clin. Invest. 130(5), 2202–2205 (2020)
Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv: 1702.08608 (2017)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019)
Viana dos Santos Santana, Í., et al.: Classification Models for COVID-19 test prioritization in Brazil: machine learning approach. J. Med. Internet Res. 23, e27293 (2021)
Mendis, B.S., Gedeon, T.D., Koczy, L.T.: Investigation of aggregation in fuzzy signatures. In: 3rd International Conference on Computational Intelligence, Robotics and Autonomous Systems (2005)
Zadeh, L.A.: Fuzzy sets. In: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, pp. 394–432 (1996)
Novák, V.: Fuzzy natural logic: towards mathematical logic of human reasoning. In: Seising, R., Trillas, E., Kacprzyk, J. (eds.) Towards the Future of Fuzzy Logic. SFSC, vol. 325, pp. 137–165. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18750-1_8
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Dorogush, A.V., Ershov, V., Gulin, A. CatBoost: gradient boosting with categorical features support. arXiv:1810.11363 (2018)
Dietterich, T.G.: Ensemble learning. In: The Handbook of Brain Theory and Neural Networks, vol. 2, pp. 110–125 (2002)
Schaffer, C.: Overfitting avoidance as bias. Mach. Learn. 10(2), 153–178 (1993)
Laurent, H., Rivest, R.L.: Constructing optimal binary decision trees is NP-complete. Inf. Process. Lett. 5(1), 15–17 (1976)
Gautier, R., Jaffre, G., Ndiaye, B.: scikit-learn-contrib/skope-rules (2020). https://github.com/scikit-learn-contrib/skope-rules
Bora, D.J., Gupta, D., Kumar, A.: A comparative study between fuzzy clustering algorithm and hard clustering algorithm. arXiv preprint arXiv:1404.6059 (2014)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Gedeon, T.D., Turner, H.S.: Explaining student grades predicted by a neural network. In: International Joint Conference on Neural Networks (1993)
Harry, S.T., Tamás, D.G.: Extracting Meaning from Neural Networks (2020). http://users.cecs.anu.edu.au/~Tom.Gedeon/pdfs/Extracting%20Meaning%20from%20Neural%20Networks.pdf
Friedman, J.H., Popescu, B.E.: Predictive learning via rule ensembles. Ann. Appl. Stat. 2(3), 916–954 (2008)
Gautier, R., Jaffre, G., Ndiaye, B.: Interpretability with diversified-by-design rules; Skope-rules, a python package (2020). http://2018.ds3-datascience-polytechnique.fr/wp-content/uploads/2018/06/DS3-309.pdf
Simpson, P.K.: Fuzzy min-max neural networks-part 1: classification. IEEE Trans. Neural Netw. 3(5), 776–786 (1992)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)
Abe, S., Lan, M.S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Syst. 3(1), 18–28 (1995)
Lekhtman, A.: Data Science in Medicine - Precision & Recall or Specificity & Sensitivity? (2019). https://towardsdatascience.com/should-i-look-at-precision-recall-or-specificity-sensitivity-3946158aace1
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Xu, X., Ding, X., Qin, Z., Liu, Y. (2021). Classification Models for Medical Data with Interpretative Rules. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_19
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