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https://doi.org/10.4...arrow_drop_down
https://doi.org/10.4018/978159...
Part of book or chapter of book . 2011 . Peer-reviewed
Data sources: Crossref
https://doi.org/10.4018/978-1-...
Part of book or chapter of book . 2008 . Peer-reviewed
Data sources: Crossref
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Decision Trees

Authors: John Wang; Dajin Wang;

Decision Trees

Abstract

Decision trees are part of the decision theory and are excellent tools in the decision-making process. Majority of decision tree learning methods were developed within the last 30 years by scholars like Quinlan, Mitchell, and Breiman, just to name a few (Ozgulbas & Koyuncugil, 2006). There are a number of methods and sophisticated software used to graphically present decision trees. Decision trees have a great number of benefits and are widely used in many business functions as well as different industries. However there are also disagreements and various concerns as to how useful decision trees really are. As technology evolves so do decision trees. Therefore not only do many controversies arise but also solutions and new proposals to these arguments.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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