Partial classification can be beneficial even for ideal separation
Yoram Baram · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1998
In the author's recent paper (ibid., vol.20, no.8 (1998)), it was shown that partial classification, which allows for indecision in certain regions of the data space, can increase a benefit function. It was also claimed that in the case of ideal separation, corresponding to the maximum likelihood criterion, partial classification cannot be more beneficial than full classification. The latter claim is false, and a counterexample is given to support the argument. The error in the original statement was caused by the omission of certain terms in the calculation of the decision probability. The erroneous result may be omitted without changing the rest of the paper. In fact, the present observation strengthens the main claim of the paper, that partial classification can be beneficial.