  This paper presents a batch classifier that has been improved from the
earlier version and fixed a mistake in the earlier paper. Two important changes
have been made. Each category is represented by a classifier, where each
classifier classifies its own subset of data rows, using batch input values to
represent the centroid. The first change is to use the category centroid as the
desired category output. When the classifier represents more than one category,
it creates a new layer and splits, to represent each category separately in the
new layer. The second change therefore, is to allow the classifier to branch to
new levels when there is a split in the data, or when some data rows are
incorrectly classified. Each layer can therefore branch like a tree - not for
distinguishing features, but for distinguishing categories. The paper then
suggests further innovations, by adding fixed value ranges through bands, for
each column or feature of the input dataset. When considering features, it is
shown that some of the data can be classified directly through fixed value
ranges, while the rest can be classified using the classifier technique. Tests
show that the method can successfully classify a diverse set of benchmark
datasets to better than the state-of-the-art. The paper also discusses a
biological analogy with neurons and neuron links.
