  FOLD-R is an automated inductive learning algorithm for learning default
rules for mixed (numerical and categorical) data. It generates an (explainable)
answer set programming (ASP) rule set for classification tasks. We present an
improved FOLD-R algorithm, called FOLD-R++, that significantly increases the
efficiency and scalability of FOLD-R by orders of magnitude. FOLD-R++ improves
upon FOLD-R without compromising or losing information in the input training
data during the encoding or feature selection phase. The FOLD-R++ algorithm is
competitive in performance with the widely-used XGBoost algorithm, however,
unlike XGBoost, the FOLD-R++ algorithm produces an explainable model. FOLD-R++
is also competitive in performance with the RIPPER system, however, on large
datasets FOLD-R++ outperforms RIPPER. We also create a powerful tool-set by
combining FOLD-R++ with s(CASP)-a goal-directed ASP execution engine-to make
predictions on new data samples using the answer set program generated by
FOLD-R++. The s(CASP) system also produces a justification for the prediction.
Experiments presented in this paper show that our improved FOLD-R++ algorithm
is a significant improvement over the original design and that the s(CASP)
system can make predictions in an efficient manner as well.
