Rule by Rule: Learning with Confidence through Vocabulary Expansion

TL;DR

This paper introduces an iterative rule learning method that expands vocabulary to reduce memory use and employs a Confidence metric to select reliable rules, enhancing rule quality in text and non-text datasets.

Contribution

The paper proposes a novel iterative rule learning approach with vocabulary expansion and a Confidence measure to improve rule robustness and efficiency.

Findings

Reduces memory consumption significantly

Improves rule reliability using Confidence metric

Effective on diverse textual and non-textual datasets

Abstract

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.