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Lightweight-oriented classification using distillation learning and spatial information from tabular data
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DOI：10.1016/j.asoc.2026.114798.png)
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Abstract
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• We propose a lightweight model that effectively captures spatial information through a distillation learning approach. • Our method outperforms traditional approaches, showing superior results on two agriculture-related datasets for wine quality. • Beyond agriculture, we tested medical clinical datasets, where the model still outperformed traditional methods, proving cross-domain potential. • Our work broadens the applicability of deep learning to tabular data, enhancing model performance and generalization capabilities.
Keywords:
Lightweight-oriented model
Distillation learning
Deep learning
Tabular data processing
Spatial information
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