A Divide-and-Conquer Genetic Programming Algorithm With Ensembles for Image Classification

Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address th...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 25; no. 6; pp. 1148 - 1162
Main Authors: Bi, Ying, Xue, Bing, Zhang, Mengjie
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
Computational efficiency
Computing costs
Computing time
Divide-and-conquer
ensemble learning
Feature extraction
feature learning
Genetic algorithms
genetic programming (GP)
Image classification
Knowledge management
Knowledge transfer
knowledge transfer (KT)
Machine learning
Sociology
Statistics
Task analysis
Training
Training data
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this article develops a divide-and-conquer GP algorithm with knowledge transfer (KT) and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divide-and-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new KT method is proposed to improve GP learning performance. A new fitness function based on log loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-the-art algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and KT in the proposed approach.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3082112