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

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Bi:TEVC2,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "A Divide-and-Conquer Genetic Programming Algorithm with Ensembles for Image Classification",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2021",
• volume = "25",
• number = "6",
• pages = "1148--1162",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Feature Learning, Knowledge Transfer, Ensemble Learning, Divide-and-Conquer, Image Classification",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2021.3082112",
• size = "15 pages",
• 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 paper develops a divide-and-conquer GP algorithm with knowledge transfer and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divideand-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new knowledge transfer 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-theart 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 knowledge transfer in the proposed approach.",
• notes = "also known as \cite{9437306}",

}

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang

Citations