Adaptive sampling for active learning with genetic programming
Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalan...
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| Published in: | Cognitive systems research Vol. 65; pp. 23 - 39 |
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01.01.2021
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| Abstract | Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths. |
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| AbstractList | Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths. |
| Author | Borgi, Amel Ben Hamida, Sana Hmida, Hmida Rukoz, Marta |
| Author_xml | – sequence: 1 givenname: Sana surname: Ben Hamida fullname: Ben Hamida, Sana email: sana.mrabet@dauphine.psl.eu organization: Université Paris Dauphine, PSL Research University, CNRS, UMR, LAMSADE, Paris 75016, France – sequence: 2 givenname: Hmida surname: Hmida fullname: Hmida, Hmida organization: Université Paris Dauphine, PSL Research University, CNRS, UMR, LAMSADE, Paris 75016, France – sequence: 3 givenname: Amel surname: Borgi fullname: Borgi, Amel email: amel.borgi@insat.rnu.tn organization: Université de Tunis El Manar, Institut Supérieur d’Informatique et Faculté des Sciences de Tunis, LR11ES14 LIPAH, Tunis 2092, Tunisia – sequence: 4 givenname: Marta surname: Rukoz fullname: Rukoz, Marta email: marta.rukoz@lamsade.dauphine.fr organization: Université Paris Dauphine, PSL Research University, CNRS, UMR, LAMSADE, Paris 75016, France |
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| Cites_doi | 10.1162/106365604773955157 10.1016/j.ejor.2007.08.008 10.1145/3188745.3188752 10.1145/347090.347110 10.1109/TMTT.2010.2090407 10.1002/9781119136378 10.1007/978-3-319-47898-2_6 10.4310/CIS.2002.v2.n1.a3 10.1007/978-3-642-17432-2_28 10.1007/978-3-540-46239-2_9 10.1115/1.4031905 10.1007/978-3-540-24621-3_22 10.1115/DETC2011-47288 10.1177/105971239700500201 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186 10.1023/A:1022673506211 10.1007/978-3-540-24840-8_12 10.1007/s10115-012-0507-8 10.1016/j.ins.2016.12.023 |
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| Keywords | Training data sampling Adaptive sampling Active learning Genetic programming Sampling frequency control Machine learning |
| Language | English |
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