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September 10, 2025ElectronicsOpen Access

Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information

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A data reduction strategy based on pointwise v-information can significantly maintain classifier performance.
Experiments show only a 0.0001% to 0.76% decline in accuracy when 10–30% of data is removed.
Using a progressive learning strategy on selected instances results in a 0.8% accuracy gain compared to traditional methods.
This approach has been adapted for various Chinese NLP tasks, showcasing its versatility and potential.

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Proposes a data reduction strategy to enhance classifier performance and training efficiency in AI, utilizing pointwise v-information.

Chen et al. (2025) studied this question.

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