Abstract
In our work, we consider the classification methods based on the model of logistic regression for positive and unlabeled data. We examine the following four methods of the posterior probability estimation, where the risk of logistic loss function is optimized, namely: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods - the joint approach, and the LassoJoint method. The objective of our study is to evaluate the accuracy, the recall, the precision and the F1-score of the considered classification methods. The corresponding assessments have been carried out on 13 machine learning model schemes by conducting some numerical experiments on selected real datasets.
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Notes
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This research was carried out with the support of the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) at the University of Warsaw, under computational allocation No. g88-1185.
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Furmańczyk, K., Paczutkowski, K., Dudziński, M., Dziewa-Dawidczyk, D. (2022). Classification Methods Based on Fitting Logistic Regression to Positive and Unlabeled Data. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_3
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