Developing higher-order networks with empirically selected units

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Abstract
The paper introduces a class of simple polynomial neural network classifiers, called mask perceptrons. A series of algorithms for practical development of such structures is outlined. It relies on ordering of input attributes with respect to their potential usefulness and heuristic driven generation and selection of hidden units (monomial terms) in order to combat the exponential explosion in the number of higher-order monomial terms to choose from. Results of tests for two popular machine learning benchmarking domains (mushroom classification and faulty LED-display), and for two nonstandard domains (spoken digit recognition and article category determination) are given. All results are compared against a number of other classifiers. A procedure for converting a mask perceptron to a classical logic production rule is outlined and shown to produce a number of 100% percent accurate simple rules after training on 6-20% of a database.