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arXiv Machine Learning

Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers

The paper presents a model‑agnostic framework that performs constrained post‑hoc error correction for binary classifiers. It searches for an interpretable conjunction of feature–threshold rules that corrects remaining false positives or false negatives while limiting newly introduced errors, using graph‑based search, depth‑dependent constraints, and a reduced‑histogram threshold evaluation. Experiments on a large binary‑classification problem show that the method can efficiently identify compact correction rules, such as a configuration that removes 90% of false positives while only sacrificing 5% of true positives.

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arXiv Machine Learning
Sep 14

Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage

The paper examines binary-choice truthfulness benchmarks, showing that systematic differences in surface-level features between correct and incorrect answers allow models to perform well without genuine reasoning. Using a six-feature logistic classifier, the authors demonstrate that such leakage is detectable and exploitable, and they find similar artifacts in multiple benchmarks. To mitigate this, they propose a cleaning method called Audit‑Prune that removes the most leakage‑reinforcing answer pairs, releasing a revised TruthfulQA dataset with reduced surface‑feature leakage.

By Foad Namjoo, Remy Ogasawara, Amirali Abdullah, Cullen Anderson, Narmeen Fatimah Oozeer, Jeff M. Phillips
llmsbenchmarks
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arXiv Machine Learning
Aug 19

Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification

The paper presents an exact constrained reformulation for direct metric optimization (DMO) in binary imbalanced classification, focusing on precision, recall, and F1-score under three settings: fixing precision to optimize recall, fixing recall to optimize precision, and optimizing F1-score. Unlike prior approaches that use smooth approximations, the authors introduce exact penalty methods to solve these problems efficiently. Experiments on benchmark datasets show that this exact reformulation and optimization (ERO) framework outperforms state‑of‑the‑art methods for all three DMO tasks.

By Le Peng, Yash Travadi, Chuan He, Ying Cui, Ju Sun
benchmarks
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arXiv AI
Aug 19

From Abductive Explanations to Global Logical Rules for Node Classification in SGCs

The paper introduces a logic-based framework that extracts global logical rules for node classification in Simple Graph Convolution (SGC) networks. It uses minimal abductive explanations—small sets of node-feature pairs that preserve a node’s predicted class—as an intermediate step. Decision trees trained on these explanations yield compact global rules that retain high fidelity to the original SGC model, as demonstrated on benchmark datasets.

By Bryan Lima Cavalcante, Thiago Alves Rocha
benchmarks
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arXiv AI
Sep 16

GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning

GraphIFE addresses the class imbalance problem in graph-structured data by tackling a quality inconsistency issue in synthesized nodes. The framework uses graph invariant learning to strengthen embedding space representations and identify invariant features, leading to improved performance on minority classes. Experiments show that GraphIFE consistently outperforms various baselines across multiple datasets.

By Fanlong Zeng, Wensheng Gan, Kangjie Chen, Philip S. Yu
rag
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