In manufacturing systems, only a small training dataset can be obtained in the early stages. A small training dataset usually leads to low learning accuracy with regard to classification of machine learning, and the knowledge derived is often fragile, and this is called the small sample problem. This research mainly aims at overcoming this problem using a special nonlinear classification technique to generate virtual samples to enlarge the training dataset for learning improvement. This research proposes a new sample generation method, named non-linear virtual sample generation (NVSG), which combines a unique group discovery technique and a virtual sample generation method using parametric equations of hypersphere. By applying a back-propagation neural network (BPN) as the learning tool, the computational experiments obtained from the simulated dataset and the real dataset quoted from the Iris Plant Database show that the learning accuracy can be significantly improved using NVSG method for very small training datasets.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence