\begin{abstract}
The CLIFF instance selection algorithm
ranks attribute values by their probability of belonging to one class, rather than any other.
It then
{\em prunes}
instances that lack the higher ranking ranges.
As a result of this pruning, CLIFF tends to increase the distance between  instances of different classes.
We show that, as a result, CLIFF also reduces {\em brittleness} (i.e. where
 minor variation in  instances leads to a
major variation in what we infer from that instance)
since
instances have
to vary more {\em before} they can change class.

We recommend CLIFF over other instance selection algorithms since
it runs in linear time (and other instance selectors are slower).
Also,
nearest neighbor inference works just as well in the pruned space, as in the full space.
Further,
when compared to other instance selectors, its inferences are far less susceptible to injected noise.
Lastly,
in a large case study wth a  state-of-the-art forensics interpretation model,
CLIFF improved the reliability of the conclusions from that model.
\end{abstract}
