# Rule by Rule: Learning with Confidence through Vocabulary Expansion

arXiv: https://arxiv.org/abs/2411.00049 · HTML: https://mlsanity.com/paper/2411.00049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection](https://mlsanity.com/paper/1902.10339/) (2019)
- [Automatic Rule Induction for Interpretable Semi-Supervised Learning](https://mlsanity.com/paper/2205.09067/) (2022)
- [Proofread: Fixes All Errors with One Tap](https://mlsanity.com/paper/2406.04523/) (2024)
- [Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples](https://mlsanity.com/paper/1802.07966/) (2018)
- [A Voting Approach for Explainable Classification with Rule Learning](https://mlsanity.com/paper/2311.07323/) (2023)
- [Rule Learning by Modularity](https://mlsanity.com/paper/2212.12335/) (2022)

## Abstract (arXiv)

In this paper, we present an innovative iterative approach to rule learning specifically designed for (but not limited to) text-based data. Our method focuses on progressively expanding the vocabulary utilized in each iteration resulting in a significant reduction of memory consumption. Moreover, we introduce a Value of Confidence as an indicator of the reliability of the generated rules. By leveraging the Value of Confidence, our approach ensures that only the most robust and trustworthy rules are retained, thereby improving the overall quality of the rule learning process. We demonstrate the effectiveness of our method through extensive experiments on various textual as well as non-textual datasets including a use case of significant interest to insurance industries, showcasing its potential for real-world applications.