Disjoint 15 1

Disjoint 15-1 is a specific task in the field of computer vision aimed at classifying images through non-overlapping feature subsets to enhance the model's generalization and robustness. The goal of this task is to ensure that each feature subset independently contributes to the final classification decision, thereby strengthening the model's adaptability to different environments and conditions. The application value of Disjoint 15-1 lies in its ability to effectively reduce overfitting and improve model performance in complex scenarios, making it suitable for multimodal data fusion and large-scale image classification, among other applications.