Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Bi:2020:PPSN,
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author = "Ying Bi and Bing Xue and Mengjie Zhang",
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title = "Evolving Deep Forest with Automatic Feature Extraction
for Image Classification Using Genetic Programming",
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booktitle = "16th International Conference on Parallel Problem
Solving from Nature, Part I",
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year = "2020",
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editor = "Thomas Baeck and Mike Preuss and Andre Deutz and
Hao Wang2 and Carola Doerr and Michael Emmerich and
Heike Trautmann",
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volume = "12269",
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series = "LNCS",
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pages = "3--18",
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address = "Leiden, Holland",
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month = "7-9 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, EvoDF,
Evolutionary deep learning, Deep forest, Image
classification, Feature extraction",
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isbn13 = "978-3-030-58111-4",
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URL = "https://openaccess.wgtn.ac.nz/articles/chapter/Evolving_deep_forest_with_automatic_feature_extraction_for_image_classification_using_genetic_programming/13158329",
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DOI = "doi:10.1007/978-3-030-58112-1_1",
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size = "14 pages",
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abstract = "Deep forest is an alternative to deep neural networks
to use multiple layers of random forests without
back-propagation for solving various problems. In this
study, we propose a genetic programming-based approach
to automatically and simultaneously evolving effective
structures of deep forest connections and extracting
informative features for image classification. First,
in the new approach we define two types of modules:
forest modules and feature extraction modules. Second,
an encoding strategy is developed to integrate forest
modules and feature extraction modules into a tree and
the search strategy is introduced to search for the
best solution. With these designs, the proposed
approach can automatically extract image features and
find forests with effective structures simultaneously
for image classification. The parameters in the forest
can be dynamically determined during the learning
process of the new approach. The results show that the
new approach can achieve better performance on the
datasets having a small number of training instances
and competitive performance on the datasets having a
large number of training instances. The analysis of
evolved solutions shows that the proposed approach uses
a smaller number of random forests over the deep forest
method.",
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notes = "tried ResNet, AlexNet, EvoDF, CNN, gcForest (8
forests), Random Forest, SVM (linear kernel), kNN on
ORL, Extend Yale B, SCENE, KTH, MNIST and CIFAR-10.
'Fig 6 The solution found by EvoDF on the MNIST
dataset' depth 8.
PPSN2020",
- }
Genetic Programming entries for
Ying Bi
Bing Xue
Mengjie Zhang
Citations