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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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 25; H. 6; S. 1148 - 1162
Hauptverfasser:	Bi, Ying, Xue, Bing, Zhang, Mengjie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Xue, Bing Zhang, Mengjie Bi, Ying
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Snippet	Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing...
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SubjectTerms	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
Title	A Divide-and-Conquer Genetic Programming Algorithm With Ensembles for Image Classification
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