Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
Computers and Operations Research
The data complexity index to construct an efficient cross-validation method
Decision Support Systems
A novel virtual sample generation method based on Gaussian distribution
Knowledge-Based Systems
A method to generate artificial 2D shape contour based in fourier transform and genetic algorithms
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Hi-index | 12.05 |
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.