The assessment of the performance of supervised classification rules by est
imating their error rate (the proportion of objects misclassified) is an im
portant area of work in statistical pattern recognition. This paper reviews
the last ten years of error rate research, bringing up to date the reviews
of Hand (1986a) and McLachlan (1987). Since those surveys were published,
old estimators have been improved, new estimators have been introduced, and
new approaches to error rate estimation have been developed. Some of this
work has led to deep insights into classification methodology and statistic
al modelling in general.