A Siamese Deep Forest

A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. It can be also regarded as an alternative to the well-known Siamese neural networks. The SDF uses a modified training set consistin...

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Bibliographic Details
Published in:Knowledge-based systems 2018-01, Vol.139, p.13-22
Main Authors: Utkin, Lev V., Ryabinin, Mikhail A.
Format: Article
Language:English
Subjects:
Artificial intelligence
Classification
Decision tree
Deep learning
Forests
Metric learning
Neural networks
Optimization
Quadratic optimization
Random forest
Siamese
Training
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Summary:A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. It can be also regarded as an alternative to the well-known Siamese neural networks. The SDF uses a modified training set consisting of concatenated pairs of vectors. Moreover, it defines the class distributions in the deep forest as the weighted sum of the tree class probabilities such that the weights are determined in order to reduce distances between similar pairs and to increase them between dissimilar points. We show that the weights can be obtained by solving a quadratic optimization problem. The SDF aims to prevent overfitting which takes place in neural networks when only limited training data are available. The numerical experiments illustrate the proposed distance metric method.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.10.006