Meta-analysis of 28 FFS studies shows experimental design choices explain 33% of variance in new method performance against baselines.
Knowledge and Information Systems 66(3)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.
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Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices
Meta-analysis of 28 FFS studies shows experimental design choices explain 33% of variance in new method performance against baselines.
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Efficient Benchmarking Is Just Feature Selection and Multiple Regression
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.