Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification

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

@Article{Ying_Bi:cybernetics1,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification",
• journal = "IEEE Transactions on Cybernetics",
• year = "2023",
• volume = "53",
• number = "2",
• pages = "1118--1132",
• month = feb,
• keywords = "genetic algorithms, genetic programming, evolutionary computation, EC, feature learning, instance selection, surrogate",
• ISSN = "2168-2275",
• DOI = "doi:10.1109/TCYB.2021.3105696",
• size = "15 pages",
• abstract = "Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.",
• notes = "Also known as \cite{9526355}",

}

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang

Citations