FAIR: Filtering of Automatically Induced Rules

arXiv:2402.15472v1 Announce Type: new
Abstract: The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data. Automatic Rule Induction (ARI) approaches circumvent this problem by automatically creating rules from features on a …

abstract algorithms annotated data arxiv availability cs.lg data diverse domain domains fair filtering machine machine learning machine learning algorithms rules set supervision training training data type