Automatic Feature Learning via Genetic Programming with Flexible Filtering for Skin Cancer Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8638

@InProceedings{DBLP:conf/cec/YuLBL25,

• author = "Kunjie Yu and Jintao Lian and Ying Bi and Jing Liang",
• title = "Automatic Feature Learning via Genetic Programming with Flexible Filtering for Skin Cancer Image Classification",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Representation learning, Training, Filtering, Noise, Feature extraction, Robustness, Computational efficiency, Skin cancer, Image classification, Skin Cancer Image Classification, Flexible Filtering",
• isbn13 = "979-8-3315-3432-5",
• timestamp = "Fri, 11 Jul 2025 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/cec/YuLBL25.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/CEC65147.2025.11042919",
• DOI = "10.1109/CEC65147.2025.11042919",
• abstract = "Skin cancer images frequently contain substantial noise, which poses challenges for effective feature extraction and classification. Although existing genetic programming (GP)-based methods exhibit adaptability to diverse tasks, they frequently lack dedicated mechanisms to effectively address noise. To overcome this limitation, this paper develops GPFF (genetic programming with a flexible filtering layer), a novel approach that reduces noise and enhances the extraction of meaningful and diverse feature representations. A novel program structure is proposed, incorporating a flexible filtering layer to enhance the reliability of feature extraction by effectively reducing noise. The flexible filtering layer incorporates a variety of image filtering functions, which capture critical image characteristics across multiple domains. By flexibly selecting and combining these filtering functions, GPFF maintains robustness across various datasets. Extensive experiments on four skin cancer datasets demonstrate that GPFF consistently outperforms five traditional feature extraction methods and three GP-based methods in most cases. Further analysis shows that the flexible filtering layer improves classification performance while achieving effective feature learning without significantly increasing computational costs, underscoring its practicality for skin cancer image classification tasks.",
• notes = "also known as \cite{yu:2025:CEC} \cite{11042919}",

}

Genetic Programming entries for Kunjie Yu Jintao Lian Ying Bi Jing Liang

Citations