This paper extends the earlier work on a one-pass categorical classifier. Specifically, it extends the design to include further corrections, by adding new layers to the classifier through a branching method. Each new layer re-classifies a subset of the data, belonging to the parent classifier only. This technique is still consistent with earlier work and neural networks, or even decision trees. With this extended design, the classifier can now achieve the high levels of accuracy reported previously. A second version then adds fixed value ranges through bands, for each column or feature of the input dataset. It is shown that some of the data can be correctly classified through using fixed value ranges only, while the rest can be classified by using the classifier technique. This can possibly present the classifier in terms of a biological model of neurons and neuron links.
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