Neural network encoding approach comparison: an empirical study

摘要

The authors report the results of an empirical study about the effect of input encoding on the performance of a neural network in the classification of numerical data. Two types of encoding schemes were studied, namely numerical encoding and bit pattern encoding. Fisher Iris data were used to evaluate the performance of various encoding approaches. It was found that encoding approaches affect a neural network's ability to extract features from the raw data. Input encoding also affects the training errors, such as maximum error, root square error, the training times and cycles needed to attain these error thresholds. It was also noted that an encoding approach that uses more input nodes to represent a single parameter generally can result in relatively lower training errors for the same training cycles.