Search-based framework for transparent non-overlapping ensemble models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Gulowaty:2022:IJCNN,
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author = "Bogdan Gulowaty and Michał Woźniak",
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booktitle = "2022 International Joint Conference on Neural Networks
(IJCNN)",
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title = "Search-based framework for transparent non-overlapping
ensemble models",
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year = "2022",
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abstract = "Due to their generalizing ability, classifier
ensembles are considered very powerful predictive
models. A typical ensemble consists of a static or
dynamic pool of classifiers and a combination method,
which translates predictions of many models into one.
The combination step is often complex and renders the
inner behavior of the whole ensemble incomprehensible
to a typical user. In this work, in the light of recent
interest in Explainable AI (XAI) research, we are
proposing a novel approach to building an interpretable
ensemble model. It is based on decision space splitting
into non-overlapping regions. Every area has an
assigned interpretable classifier and its boundaries
are selected using the genetic programming approach. We
experimentally evaluate the proposed method and compare
it to Decision Tree and Random Forest. The results show
that the proposed approach is competitive with the
state-of-the-art techniques and prone to further
expansion.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/IJCNN55064.2022.9892360",
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ISSN = "2161-4407",
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month = jul,
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notes = "Also known as \cite{9892360}",
- }
Genetic Programming entries for
Bogdan Gulowaty
Michał Woźniak
Citations