Abstract:
A motivational problem that arises in machine learning is to estimate out-of-sample
error rate of a k-nearest neighbor classi er. Without having any prior knowledge of
distribution or any assumption of distribution it is required to estimate the maximum
probability of misclassi cation of an unlabeled sample. Previous works include the
assumption on data distribution as identical and independent distribution (i.i.d.).
This method works for binary classi cation only. Our proposed algorithm is applicable
for any data sets without having any knowledge of the underlying data distribution.
Our algorithm will search the misclassi cation region in the data set and calculate
the bound for an unlabeled test sample. Our method will always detect the class
overlapping region within the data set irrespective of balanced and imbalanced. Our
method is also designed for both two-class and multi-class data sets. Our experiments
includes the bound validation for di erent scenarios. We have tested for random k
value and xed range of k. Also veri ed for balanced and imbalanced data sets.
We also demonstrated to nd optimal sets of k values where classi er error will be
minimized.
