Feature Optimization and Dropout in Genetic Programming for Data-Limited Image Classification

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@Article{lee:2024:Mathematics,

• author = "Chan Min Lee and Chang Wook Ahn and Man-Je Kim",
• title = "Feature Optimization and Dropout in Genetic Programming for Data-Limited Image Classification",
• journal = "Mathematics",
• year = "2024",
• volume = "12",
• number = "23",
• pages = "Article No. 3661",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-7390",
• URL = "https://www.mdpi.com/2227-7390/12/23/3661",
• DOI = "doi:10.3390/math12233661",
• abstract = "Image classification in data-limited environments presents a significant challenge, as collecting and labeling large image datasets in real-world applications is often costly and time-consuming. This has led to increasing interest in developing models under data-constrained conditions. This paper introduces the Feature Optimisation and Dropout in Genetic Programming (FOD-GP) framework, which addresses this issue by leveraging Genetic Programming (GP) to evolve models automatically. FOD-GP incorporates feature optimisation and adaptive dropout techniques to improve overall performance. Experimental evaluations on benchmark datasets, including CIFAR10, FMNIST, and SVHN, demonstrate that FOD-GP improves training efficiency. In particular, FOD-GP achieves up to a 12percent increase in classification accuracy over traditional methods. The effectiveness of the proposed framework is validated through statistical analysis, confirming its practicality for image classification. These findings establish a foundation for future advancements in data-limited and interpretable machine learning, offering a scalable solution for complex classification tasks.",
• notes = "also known as \cite{math12233661}",

}

Genetic Programming entries for Chan Min Lee Chang Wook Ahn Man-Je Kim

Citations