This paper introduces a novel approach to binary classifiers by incorporating per-class regularization hyperparameters within Gabriel graph-based systems. The method enhances outlier elimination and addresses class imbalance by allowing flexible thresholds for majority and minority classes. Experimental results using the Friedman test indicate that this adaptive regularization improves classifier performance. AI
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IMPACT Introduces a new regularization technique for classifiers that could improve performance on imbalanced datasets and with outliers.
RANK_REASON This is a research paper published on arXiv detailing a new classification method.