Multi-dimensional data construction method with its application to learning from small-sample-sets

Insufficient training data is one of the major problems in neural network learning, because it leads to poor learning performance. In order to enhance an intelligent learning process, it is necessary to exploit the features of the problem from the available information even with limited scale. Due to the shortcomings of the existing methods for data generation; and also in general, a problem is described by multiple attributes, this study has first extended the developed one-dimensional Data Construction Method (DCM) for virtual data generation to multidimensional continuous space as denoted by m-DCM. Then, sensitivity analysis and numerical illustration have been carried out. By incorporating m-DCM into a supervised neural network learning process, we have shown to overcome the existing unbounded and immeasurable problems and provided a better learning performance in a comparative manner.