Experiments on the Costs and Benefits of Windowing in ID3
Elsevier eBooksPublished 1 January 1988
Jarryl Wirth, Jason Catlett
Citations52
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TL;DR
It is concluded that in noisy domains (where ID3 is now commonly used), windowing should be avoided, and the use of windowing considerably increased the CPU requirements of ID3 but produced no significant benefits.
Abstract
Quinlan's machine learning system ID3 uses a method called windowing to deal economically with large training sets. This paper describes a series of experiments performed to investigate the merits of this technique. In nearly every experiment the use of windowing considerably increased the CPU requirements of ID3, but produced no significant benefits. We conclude that in noisy domains (where ID3 is now commonly used), windowing should be avoided.
Keywords
Computer Science
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