In Ross Quinlan's seminal paper Induction of Decision Trees, Quinlan summarizes the current state of machine learning in 1985 and loudly introduces the ID3 decision algorithm in the context of its peers in the field. However, he also states this halfway into the paper:
An alternative method based on the chi-square test for stochastic independence has been found to be more useful.
And further in:
Hart notes, however, that the chi-square test is valid only when the expected values of $p' i$ and $n'i$ are uniformly larger than four. This condition could be violated by a set C of objects either when C is small or when few objects in C have a particular value of some attribute, and it is not clear how such sets would be handled. No empirical results with this approach are yet available.
The name for this modification of the algorithm is not given, though. Is there an accepted name for this modification of ID3 (which is usually dependent on Shannon entropy to perform branching)? It seems like something that should be obvious (eg, ID3.1.x), but I cannot find a formal source.
I've also taken the liberty of perusing and studying the meta links within the FAQ of this fine site, and this didn't seem to fall into any of the bins for obviousness or non-research level, nor did it appear to be a good fit for other StackExchange sites. It is, however, a simple question that I could not find the answer to in any of the common sources.
This is directly pertinent to my research: I'm compiling another optimization that bridges the gap between generalized Shannon mutual information and Pearson's chi-squared test for independence in the context of TDIDT, then applying it to multivariate characteristics in cancer data as an alternative to random forests. As such, working definitions for my terms are greatly useful, insofar as they might lead to further papers I am not familiar with.