\comment{
First, in \cite{me02c} the numeric output classes were
discretized using a complex method that produced 20 different
discretized output classes.
Recall that DDP outputs the cost and benefit
for a given set of applied mitigations.
Since ITL requires a single output class, these
two output variables from DDP need to be combined into
single attribute.
\fig{utils1} shows how the orignal output classes
were transformed to the single output class that 
was passed to the ITL method.
\begin{figure}
\begin{center}
{\footnotesize {\bf scoring
function:}\begin{tabular}{c|rrrr}\multicolumn{1}{c}{}&\multicolumn{4}{c}{Cost}\\\cline{2-5}
Benefit       &  400K    &600K    & 800K    & 1,000K    \\\hline
 250         &    1   &  2     &  3    & 4          \\
 200           &   5   &   6   &   7   &  8         \\
 150           &   9   &   10   &  11  & 12       \\
 100         &   13   &   14   &  15   &  16          \\
  50         &   17   &  18   &   19   &  20       \\ \hline
\end{tabular}}
\end{center}
\caption{Class scoring function.}
\label{fig:utils1}
\end{figure}

In this study we investigated using {\it BoRe}.
BoRe (Best Or REst) is a geometric discretization
method that we beleive has several advantages over
the previous method.
The orignal intuition about BoRe is that we are
trying to drive the simulator into a desired location
in the output space, hence the learner doesn't need
a multitude of output classes that capture varying
degrees of ``goodness''.
Rather, it only needs to know what instances have
a high worth and which ones don't.
For that reason BoRe divides the instances into only
two classes, {\it good} and {\it bad}.
It is a geometric technique because it determines
output class membership by an instance's relative
euclidean distance to a {\it sweet spot}.
The sweet spot is the point in the output space
where a user wishes to find a solution.
With DDP's outputs of cost and benefit the
obvious sweet spot is the highest possible benefit
and zero cost.
Note that no solution could actually be located at
this point in the output space.
The existence of a solution at the sweet spot is
not necessery.
It only serves as the ``perfect'' solution, against
which all others can be measured.
Operationally both the cost and benefit value were
normalized before calculating the distance
from the sweet spot, so that both values
had the range $[0, 1]$.
Two  implementation advantages that BoRe has are,
1) it can be adapted to any number of dimensions
without having to construct any table and
2) since there is only one class boundary
the user doesn't have to decide where the boundaries
should be or even how many output classes the discretization
should produce.
}