Genetic Programming with A New Representationto Automatically Learn Features and EvolveEnsembles for Image Classification.pdf (1.85 MB)

Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification

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journal contribution
posted on 29.10.2020, 00:57 by Ying Bi, Bing Xue, Mengjie Zhang
Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity.

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Preferred citation

Bi, Y., Xue, B. & Zhang, M. (2020). Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification. IEEE Transactions on Cybernetics, PP(99), 1-15. https://doi.org/10.1109/tcyb.2020.2964566

Journal title

IEEE Transactions on Cybernetics

Volume

PP

Issue

99

Publication date

01/01/2020

Pagination

1-15

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

ISSN

2168-2267

eISSN

2168-2275

Language

en

Exports