Using a Small Number of Training Instances in Genetic Programming for Face Image Classification

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@Article{Bi:2022:InformationSciences,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "Using a Small Number of Training Instances in Genetic Programming for Face Image Classification",
• journal = "Information Sciences",
• year = "2022",
• volume = "593",
• pages = "488--504",
• month = may,
• keywords = "genetic algorithms, genetic programming, MOGP, Image classification, Fitness measure, Small data, Evolutionary computation",
• ISSN = "0020-0255",
• URL = "https://www.sciencedirect.com/science/article/abs/pii/S0020025522000871",
• DOI = "doi:10.1016/j.ins.2022.01.055",
• abstract = "Classifying faces is a difficult task due to image variations in illumination, occlusion, pose, expression, etc. Typically, it is challenging to build a generalised classifier when the training data is small, which can result in poor generalisation. This paper proposes a new approach for the classification of face images based on multi-objective genetic programming (MOGP). In MOGP, image descriptors that extract effective features are automatically evolved by optimising two different objectives at the same time: the accuracy and the distance measure. The distance measure is a new measure intended to enhance generalisation of learned features and/or classifiers. The performance of MOGP is evaluated on eight face datasets. The results show that MOGP significantly outperforms 17 competitive methods.",
• notes = "also known as \cite{BI2022488}",

}

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang

Citations