Adaptive Sampling for Active Learning with Genetic Programming
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gp-bibliography.bib Revision:1.8081
- @Article{HAMIDA:2020:CSR,
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author = "Sana {Ben Hamida} and Hmida Hmida and Amel Borgi and
Marta Rukoz",
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title = "Adaptive Sampling for Active Learning with Genetic
Programming",
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journal = "Cognitive Systems Research",
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year = "2020",
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ISSN = "1389-0417",
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DOI = "doi:10.1016/j.cogsys.2020.08.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S1389041720300541",
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keywords = "genetic algorithms, genetic programming, Machine
Learning, Active Learning, Training data sampling,
Adaptive sampling, Sampling frequency control",
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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",
- }
Genetic Programming entries for
Sana Ben Hamida
Hmida Hmida
Amel Borgi
Marta Rukoz
Citations