anytime algorithms for learning anytime classifiers saher ... - Technion

Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008
Chapter 6
Related Work
While to the best of our knowledge no other work has tried to design an anytime
algorithm for learning anytime decision trees in particular, there are several related
works that warrant discussion here. We consider first works that deal with
non-greedy, cost-insensitive decision tree inducers. We then discuss algorithms
for anytime induction of other cost-insensitive models. Next we discuss works on
cost-sensitive and resource-bounded classification, and finally we consider methods
that focus on aspects of the learning process other than the model-induction
phase.
6.1 Non-greedy Cost-insensitive Decision Tree
Inducers
Several other algorithms for induction of single, cost-insensitive decision trees can
either be considered anytime algorithms or can be converted into them with relative
ease. We view the recently introduced skewing approach as the most relevant
to our LSID3. Therefore, we focus first on the differences between skewing and
our anytime appraoch, and then we consider other cost-insensitive tree learners.
6.1.1 Skewing
The skewing approach was presented as an efficient alternative to lookahead (Page
& Ray, 2003). In our discussion, we analyze the original skewing algorithm, which
is applicable only to binary attributes. This analysis, however, holds also for the
other versions of skewing (see Section 3.7.6), since they are based on the same
ideas.
Skewing relies on the hypothesis that, when learning a hard concept, it may
be easier for a greedy decision tree inducer to pick a relevant attribute if the
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