Muhammad Ahmad1,*, Manuel Mazzara2, Salvatore Distefano3, Adil Mehmood Khan4, Hamad Ahmed Altuwaijri5
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 503-532, 2024, DOI:10.32604/cmc.2024.056318
- 15 October 2024
Abstract Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new examples. This paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification (HSIC). By separating training, validation, and test data without overlap, the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was… More >
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