Rule by Rule: Learning with Confidence through Vocabulary Expansion
This work addresses rule learning efficiency and reliability for domains like text processing and insurance, though it appears incremental with iterative improvements to existing methods.
The paper tackles the problem of rule learning by proposing an iterative approach that expands vocabulary to reduce memory consumption and uses a Value of Confidence metric to retain only robust rules, demonstrating effectiveness on textual and non-textual datasets including insurance industry applications.
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.