- author = "Ying Bi and Bing Xue and Mengjie Zhang",
- title = "Dual-Tree Genetic Programming for Few-Shot Image Classification",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2022",
- volume = "26",
- number = "3",
- pages = "555--569",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Representation, Fitness Evaluation, Few-Shot Learning, Image Classification",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2021.3100576",
- size = "15 pages",
- abstract = "Few-shot image classification is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalisation performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This paper proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for few-shot image classification. The dual-tree representation allows the proposed approach to have better search ability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalisation performance. The proposed approach can deal with different types of few-shot image classification tasks with various numbers of classes and different",
-
notes = "also known as \cite{9499117}
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand",
