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An automated ensemble learning framework using genetic programming for image classification

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conference contribution
posted on 11.02.2021, 02:25 by Ying BiYing Bi, Bing XueBing Xue, Mengjie ZhangMengjie Zhang
© 2019 Association for Computing Machinery. An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner. Genetic programming (GP) has been applied to construct ensembles using different strategies such as bagging and boosting. However, no GP-based ensemble methods focus on dealing with image classification, which is a challenging task in computer vision and machine learning. This paper proposes an automated ensemble learning framework using GP (EGP) for image classification. The new method integrates feature learning, classification function selection, classifier training, and combination into a single program tree. To achieve this, a novel program structure, a new function set and a new terminal set are developed in EGP. The performance of EGP is examined on nine different image classification data sets of varying difficulty and compared with a large number of commonly used methods including recently published methods. The results demonstrate that EGP achieves better performance than most competitive methods. Further analysis reveals that EGP evolves good ensembles simultaneously balancing diversity and accuracy. To the best of our knowledge, this study is the first work using GP to automatically generate ensembles for image classification.

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Preferred citation

Bi, Y., Xue, B. & Zhang, M. (2019, July). An automated ensemble learning framework using genetic programming for image classification. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference GECCO '19: Genetic and Evolutionary Computation Conference (pp. 365-373). ACM. https://doi.org/10.1145/3321707.3321750

Conference name

GECCO '19: Genetic and Evolutionary Computation Conference

Title of proceedings

GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference

Publication or Presentation Year

13/07/2019

Pagination

365-373

Publisher

ACM

Publication status

Published