
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…
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Taxonomy
TopicsImbalanced Data Classification Techniques · Machine Learning and Algorithms · Machine Learning and Data Classification
