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I'm searching for a paper or book that list several possible measures for deciding which variable to split a Decision Tree at.

The basic ones are well known and very well documented in several sources:

  • Entropy (or Information Gain)
  • Gini impurity
  • Classification Error
  • Variance reduction

But I suppose people come up with all kinds of other split criteria all the time, don't they? Has someone ever collected a list? Preferably with some explanations about the use case and particular advantages (and disadvantages) of the new split criterions.

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