\section{Introduction}
The principal goal of forensic evaluation models is to check that evidence found at a crime scene is (dis)similar to evidence found on a suspect. In creating these models, attention is given to the significance level of the solution however the \emph{brittleness} level is never considered. The \emph{brittleness} level is a measure of whether a solution comes from a region of similar solutions or from a region of dissimilar solutions. We contend that a solution coming from a region with a low level of brittleness i.e. a region of similar solutions, is much better that one from a high level of brittleness - a region of dissimilar solutions.

The concept of \emph{brittleness} is not a stranger to the world of forensic science, in fact it is recognized as the ``fall-off-the-cliff-effect", a term coined by Ken Smalldon. In other words, Smalldon recognized that tiny changes in input data could lead to a massive change in the output. Although Walsh \cite{Walsh94} worked on reducing the brittleness in his model, to the best of our knowledge, no work been done to quantify brittleness in current forensic models or to recognize and eliminate the causes of brittleness in these models.

In our studies of forensic models for evaluation particularly in the sub-field of glass forensics, we conjecture that brittleness is caused by the following:

%\begin{itemize}
\be
\item A tiny error(s) in the collection of data;
\item Inappropriate statistical assumptions, such as assuming that the distributions of refractive indices of glass collected at a crime scene or a suspect obeys the properties of a normal distribution; 
\item and the use of measured parameters from surveys to calculate the \emph{frequency of occurrence} of trace evidence in a population
\ee
%\end{itemize}

In this work we quickly eliminate the two(2) latter causes of brittleness by using simple classification methods such as k-nearest neighbor (KNN) which are neither concerned with the distribution of data nor the frequency of occurrence of the data in a population. To reduce the effects of errors in data collection, a novel prototype learning algorithm (PLA) is used to augment KNN. Basically this PLA selects samples from the data which best represents the region or neighborhood it comes from. In other words, we expect that samples which contain errors would be poor representatives and would therefore be eliminated from further analysis. This leads to neighbourhoods with different outcomes being futher apart from each other.

%In the end our goal for this work is threefold. First we want to show the forensic scientist the importance of reporting the brittleness level of their models. Second, to encourage them to seek out and eliminate the causes of brittleness in their models and third, for those causes which cannot be eliminated

In the end our goal for this work is threefold. First we want to develop a new generation of forensic models which avoids inappropriate statistical assumptions. Second, the new models must not be \emph{brittle}, so that they do not change their interpretation without sufficient evidence and third, provide not only an interpretation of the evidence but also a measure of how reliable the interpretation is, in other words, what is the brittleness level of the model.

Our research is guided by the following research questions:

\begin{itemize}
\item Are the results of using KNN better or comparable to current models which use statistical assumptions and surveys
\item Does prototype learning reduce brittleness?
\item Do the results of applying a PLA differ significantly from results of not applying a PLA?
\end{itemize}


The remainder of this paper is organized as follows: Section 2 gives some background and motivation for this work, while Section 3 explores the \emph{brittleness} design and operation of CLIFF. Section 4 presents a detailed description of the experimental procedure followed to analyze the data using CLIFF. Finally, in Section 5, conclusions are presented.