\begin{abstract}
Nearest neighbor algorithms face many drawbacks. These drawbacks include, high computation costs, large storage requirement and low tolerance to noise. Unfortunately, the literature fails to mention the drawback of brittleness. Brittleness is a measure of whether a solution (pre-dicted target class) comes from a region of similar solutions or from a region of dissimilar solutions. To reduce brittleness, we turn to instance selection and introduce CLIFF. The CLIFF instance selector operates in linear time and uses a ranking measure which ranks the values of each attribute in a training set. The values with the highest ranks are then used as a criteria to select instances which obey the criteria. Intuitively, these selected instances best represent their region or neighborhood. With nine(9) standard data-sets from the UCI repository \cite{Frank+Asuncion:2010}, the outcome of this work demonstrate that for most cases, CLIFF is statistically the same as or better than those from 1NN rule classifier as well as three other instance selectors. Finally, in the forensic case study a data-set composed of the infrared spectra of the clear coat layer of a range of cars is assessed. The performance analysis showed that it is strong with near 100\% of the validation set finding the right target. 
\end{abstract}

