Modeling PU learning using probabilistic logic programming
The goal of learning from positive and unlabeled (PU) examples is to learn a classifier that predicts the posterior class probability. The challenge is that the available labels in the data are determined by (1) the true class, and (2) the labeling mechanism that selects which positive examples get...
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| Published in: | Machine learning Vol. 113; no. 3; pp. 1351 - 1372 |
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| Language: | English |
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| ISSN: | 0885-6125, 1573-0565, 1573-0565 |
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| Abstract | The goal of learning from positive and unlabeled (PU) examples is to learn a classifier that predicts the posterior class probability. The challenge is that the available labels in the data are determined by (1) the true class, and (2) the labeling mechanism that selects which positive examples get labeled, where often certain examples have a higher probability to be selected than others. Incorrectly assuming an unbiased labeling mechanism leads to learning a biased classifier. Yet, this is what most existing methods do. A handful of methods makes more realistic assumptions, but they are either so general that it is impossible to distinguish between the effects of the true classification and of the labeling mechanism, or too restrictive to correctly model the real situation, or require knowledge that is typically unavailable. This paper studies how to formulate and integrate more realistic assumptions for learning better classifiers, by exploiting the strengths of probabilistic logic programming (PLP). Concretely, (1) we propose PU ProbLog: a PLP-based general method that allows to (partially) model the labeling mechanism. (2) We show that our method generalizes existing methods, in the sense that it can model the same assumptions. (3) Thanks to the use of PLP, our method supports also PU learning in relational domains. (4) Our empirical analysis shows that partially modeling the labeling bias, improves the learned classifiers. |
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| AbstractList | The goal of learning from positive and unlabeled (PU) examples is to learn a classifier that predicts the posterior class probability. The challenge is that the available labels in the data are determined by (1) the true class, and (2) the labeling mechanism that selects which positive examples get labeled, where often certain examples have a higher probability to be selected than others. Incorrectly assuming an unbiased labeling mechanism leads to learning a biased classifier. Yet, this is what most existing methods do. A handful of methods makes more realistic assumptions, but they are either so general that it is impossible to distinguish between the effects of the true classification and of the labeling mechanism, or too restrictive to correctly model the real situation, or require knowledge that is typically unavailable. This paper studies how to formulate and integrate more realistic assumptions for learning better classifiers, by exploiting the strengths of probabilistic logic programming (PLP). Concretely, (1) we propose PU ProbLog: a PLP-based general method that allows to (partially) model the labeling mechanism. (2) We show that our method generalizes existing methods, in the sense that it can model the same assumptions. (3) Thanks to the use of PLP, our method supports also PU learning in relational domains. (4) Our empirical analysis shows that partially modeling the labeling bias, improves the learned classifiers. |
| Author | De Raedt, Luc Bekker, Jessa Verreet, Victor |
| Author_xml | – sequence: 1 givenname: Victor orcidid: 0000-0003-2595-7207 surname: Verreet fullname: Verreet, Victor email: victor.verreet@kuleuven.be organization: Department of Computer Science, Leuven.AI, KU Leuven – sequence: 2 givenname: Luc surname: De Raedt fullname: De Raedt, Luc organization: Department of Computer Science, Leuven.AI, KU Leuven, AASS, Örebro University – sequence: 3 givenname: Jessa surname: Bekker fullname: Bekker, Jessa organization: Department of Computer Science, Leuven.AI, KU Leuven |
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| Cites_doi | 10.1609/aaai.v32i1.11715 10.1007/s10994-020-05877-5 10.1109/TPAMI.2021.3061456 10.1016/S0004-3702(98)00034-4 10.1609/aaai.v36i4.20332 10.1007/s10994-015-5481-4 10.1109/IEEECONF44664.2019.9048765 10.1145/1401890.1401920 10.1007/978-3-319-78090-0_2 10.1609/aaai.v36i6.20624 10.7551/mitpress/4298.003.0069 10.1007/978-3-031-01333-1_30 10.1145/2939672.2939744 10.1007/978-3-642-23780-5_47 10.1017/S1471068414000076 10.1609/aaai.v28i1.9072 10.1016/j.patrec.2013.06.010 10.1137/1.9781611977172.3 10.1609/aaai.v34i04.5848 10.1109/ICDM.2003.1250918 10.1007/978-3-030-46147-8_5 |
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| SubjectTerms | Artificial Intelligence Classifiers Computer Science Control Empirical analysis Labeling Labels Learning Logic programming Machine Learning Mechatronics Modeling Modelling Natural Language Processing (NLP) Positive unlabeled learning Probabilistic logic programming Robotics Simulation and Modeling Special Issue on Learning and Reasoning 2022 Statistical analysis Unidentifiability Weak supervision |
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| Title | Modeling PU learning using probabilistic logic programming |
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