Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification
T Zhang, Ying Bi, Jing Liang, Bing Xue, Mengjie Zhang · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024
Image classification is challenging due to the high dimensionality and large variations of the image data. As an effective method in image classification, feature learning can be considered a multiobjective problem of maximizing the classification accuracy and minimizing the number of learned features. Existing multi-objective genetic programming (MOGP) methods directly apply the multiobjective techniques to GP without considering the characteristics of feature learning tasks. Therefore, this paper proposes a decomposition-based MOGP approach with a global replacement strategy to feature learning in image classification. The proposed approach is compared with existing MOGP methods and the experimental results demonstrate the effectiveness of the proposed approach.