Integrative Windowing
  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.
