Semi-supervised online structure learning for composite event recognition
Online structure learning approaches, such as those stemming from statistical relational learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We p...
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| Veröffentlicht in: | Machine learning Jg. 108; H. 7; S. 1085 - 1110 |
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01.07.2019
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | Online structure learning approaches, such as those stemming from statistical relational learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system. |
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| AbstractList | Online structure learning approaches, such as those stemming from statistical relational learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system. |
| Author | Michelioudakis, Evangelos Paliouras, Georgios Artikis, Alexander |
| Author_xml | – sequence: 1 givenname: Evangelos orcidid: 0000-0002-8133-7347 surname: Michelioudakis fullname: Michelioudakis, Evangelos email: vagmcs@iit.demokritos.gr organization: Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Institute of Informatics and Telecommunications, National Center for Scientific Research “Demokritos” – sequence: 2 givenname: Alexander surname: Artikis fullname: Artikis, Alexander organization: Department of Maritime Studies, University of Piraeus, Institute of Informatics and Telecommunications, National Center for Scientific Research “Demokritos” – sequence: 3 givenname: Georgios surname: Paliouras fullname: Paliouras, Georgios organization: Institute of Informatics and Telecommunications, National Center for Scientific Research “Demokritos” |
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| CitedBy_id | crossref_primary_10_1017_S147106841900022X crossref_primary_10_1007_s10472_019_09664_4 crossref_primary_10_1007_s10994_023_06447_1 crossref_primary_10_1007_s00500_020_04967_9 crossref_primary_10_1007_s10553_023_01531_7 crossref_primary_10_1109_ACCESS_2020_3040408 crossref_primary_10_1016_j_ijar_2023_108993 crossref_primary_10_1002_int_22690 |
| Cites_doi | 10.1016/S0004-3702(98)00034-4 10.1007/BF03037227 10.1007/BF03037383 10.1145/2699916 10.1080/01621459.1963.10500830 10.1145/2187671.2187677 10.1198/106186008X344748 10.1002/nav.3800020109 10.1007/s11721-015-0116-8 10.1613/jair.1509 10.3233/IDA-2003-7305 10.1017/S1471068416000260 10.1007/s10115-011-0406-4 10.1109/TKDE.2005.186 10.1109/TKDE.2010.36 10.1017/S0269888912000264 10.1007/s10707-016-0266-x 10.2200/S00196ED1V01Y200906AIM006 10.1109/34.588021 10.1145/3117809 10.1007/s10994-006-5833-1 10.1007/978-3-540-68856-3 10.1023/A:1007361123060 10.1016/j.knosys.2012.04.021 10.1109/TKDE.2014.2356476 10.3115/981658.981684 10.1023/A:1022689900470 10.1145/347090.347107 10.1007/978-3-642-23783-6_6 10.1007/978-3-319-46128-1_15 10.1145/3093742.3093912 10.1007/978-3-319-63342-8_3 |
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| Keywords | Graph-cut minimisation First-order logic distance Event recognition Semi-supervised learning Event Calculus Online structure learning |
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| Title | Semi-supervised online structure learning for composite event recognition |
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