A self-improving classifier design for high-dimensional data analysis with a limited training data set

In this paper, we propose a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples, referred as semi-labeled samples, in addition to the original training samples iteratively. In order to control the influence of semi-labeled samples, the proposed method gives full weight to the training samples and reduced weight to semi-labeled samples. Experimental results show that starting with a small training set this proposed adaptive classifier can improve the classification accuracy as well as representation of estimated statistics to a practically significant extent iteratively.