  Recently, a multi-level fuzzy min max neural network (MLF) was proposed,
which improves the classification accuracy by handling an overlapped region
(area of confusion) with the help of a tree structure. In this brief, an
extension of MLF is proposed which defines a new boundary region, where the
previously proposed methods mark decisions with less confidence and hence
misclassification is more frequent. A methodology to classify patterns more
accurately is presented. Our work enhances the testing procedure by means of
data centroids. We exhibit an illustrative example, clearly highlighting the
advantage of our approach. Results on standard datasets are also presented to
evidentially prove a consistent improvement in the classification rate.
