An Experiment with Bands and Dimensions in Classifiers

This paper presents a new version of an oscillating error classifier that has added fixed value ranges through bands, for each column or feature of the input dataset. An earlier version of the classifier added branches [8] to a categorical classification technique that allows the error update to be independent for each column value and can therefore oscillate around the desired output, reducing to some minimum. Because that classifier works off averaged values, it may be the case that some data can be classified directly, without it having to be sorted by weight sets, for example. The averaged value is simply 1 value for a whole range of actual input values and so maybe a value band can represent that range as a fixed set of boundaries. It may also be possible to construct these fixed boundaries for single dimensions, when much more complex hypercubes are not required. It is shown that some of the data can in fact be correctly classified through using fixed value ranges only, while the rest can be classified by using the classifiers. With the idea of these fixed bands that do not process very much, plus the more complex classifiers, the paper also presents the whole system in terms of a biological model of neurons and neuron links.