An Accelerator for Rule Induction in Fuzzy Rough Theory

TL;DR

This paper introduces a novel accelerator for rule induction in fuzzy rough theory that significantly reduces computation time while maintaining accuracy, addressing the challenge of big data in rule-based classifiers.

Contribution

It proposes a new acceleration technique using a Key Set and consistence degree to speed up rule induction without losing result fidelity.

Findings

Accelerator achieves faster rule induction on large datasets.

The method maintains the same results as traditional unaccelerated approaches.

Experiments confirm significant speed improvements with preserved accuracy.

Abstract

Rule-based classifier, that extract a subset of induced rules to efficiently learn/mine while preserving the discernibility information, plays a crucial role in human-explainable artificial intelligence. However, in this era of big data, rule induction on the whole datasets is computationally intensive. So far, to the best of our knowledge, no known method focusing on accelerating rule induction has been reported. This is first study to consider the acceleration technique to reduce the scale of computation in rule induction. We propose an accelerator for rule induction based on fuzzy rough theory; the accelerator can avoid redundant computation and accelerate the building of a rule classifier. First, a rule induction method based on consistence degree, called Consistence-based Value Reduction (CVR), is proposed and used as basis to accelerate. Second, we introduce a compacted search space termed Key Set, which only contains the key instances required to update the induced rule, to conduct value reduction. The monotonicity of Key Set ensures the feasibility of our accelerator. Third, a rule-induction accelerator is designed based on Key Set, and it is theoretically guaranteed to display the same results as the unaccelerated version. Specifically, the rank preservation property of Key Set ensures consistency between the rule induction achieved by the accelerator and the unaccelerated method. Finally, extensive experiments demonstrate that the proposed accelerator can perform remarkably faster than the unaccelerated rule-based classifier methods, especially on datasets with numerous instances.