通过词汇迭代扩展与置信度评估，提升规则学习的效率与可靠性。
Rule by Rule: Learning with Confidence through Vocabulary Expansion
- 每轮迭代扩展词汇表，降低内存占用
- 引入置信度指标筛选高可靠规则
- 适用于文本及非文本数据，适合保险行业应用
本文提出一种创新的迭代式规则学习方法，专为（但不限于）文本数据设计。该方法通过逐轮扩展词汇表，显著降低内存消耗。同时，引入置信度值作为生成规则可靠性的指标，仅保留最稳健可信的规则，从而提升整体规则学习质量。我们在多种文本及非文本数据集上进行了大量实验，包括保险行业具有实际意义的应用案例，验证了该方法在真实场景中的潜力。
原文摘要 · Abstract (English)
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.
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