  Abc-boost is a new line of boosting algorithms for multi-class
classification, by utilizing the commonly used sum-to-zero constraint. To
implement abc-boost, a base class must be identified at each boosting step.
Prior studies used a very expensive procedure based on exhaustive search for
determining the base class at each boosting step. Good testing performances of
abc-boost (implemented as abc-mart and abc-logitboost) on a variety of datasets
were reported.
  For large datasets, however, the exhaustive search strategy adopted in prior
abc-boost algorithms can be too prohibitive. To overcome this serious
limitation, this paper suggests a heuristic by introducing Gaps when computing
the base class during training. That is, we update the choice of the base class
only for every $G$ boosting steps (i.e., G=1 in prior studies). We test this
idea on large datasets (Covertype and Poker) as well as datasets of moderate
sizes. Our preliminary results are very encouraging. On the large datasets,
even with G=100 (or larger), there is essentially no loss of test accuracy. On
the moderate datasets, no obvious loss of test accuracy is observed when G<=
20~50. Therefore, aided by this heuristic, it is promising that abc-boost will
be a practical tool for accurate multi-class classification.
