Today I read a paper titled “Integrative Windowing”
The abstract is:
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..