The cross-interval reconstruction and heuristic calculation to deal with the continuous-valued attribute in the learning process

Abstract

Discretization is a widely used technique for organizing and simplifying continuous-valued data in classification problems, facilitating subsequent analytical applications. However, the lack of an effective approach for addressing soft boundary classification can complicate the interpretation and accuracy of results. This paper focuses on discretization methods within soft boundary classification environments, aiming to improve classification outcomes and enhance interpretability for specific applications. Building on rule-based discretization, we propose a novel approach to enhance the handling of soft boundary classification. A detailed illustrative example is provided to demonstrate the effectiveness of the proposed Cross-Interval Recursion (CIR) method and Heuristic Cross-Interval Recursion (HCIR) algorithm. Our results show that the CIR-based rule discretization method and its evaluation mechanism effectively mitigate noise interference from class boundary points, improving interpretability and promoting greater generalization in soft boundary classification. The performance of our algorithm outperforms existing methods, including EqQua-CIR, EqVal-CIR, and other rule-based discretization techniques, particularly in terms of classification accuracy when dealing with boundary points at high granularity. When compared to classic classifiers and other rule-based discretization approaches, our method demonstrates that rule-based classifiers are more effective than direct approaches in handling soft boundary issues. Furthermore, the alignment between the classifier and the sample data plays a critical role in determining classification performance. Our approach offers significant potential as a breakthrough in addressing soft boundary classification of continuous-valued attributes, leveraging interval reconstruction, and enhancing classification robustness.