Integrative Windowing

TL;DR

This paper introduces integrative windowing, a novel algorithm that improves rule learning efficiency by integrating rules immediately after discovery, demonstrating significant run-time gains especially in noise-free domains.

Contribution

The paper presents integrative windowing, a new algorithm that exploits rule independence to enhance rule learning efficiency and reduce re-learning time.

Findings

Integrative windowing achieves substantial run-time gains in noise-free domains.

The algorithm can handle simple noisy domains with improved efficiency.

Rule independence allows for immediate integration, reducing re-learning.

Abstract

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.