Improvement of the Parzen classifier in small training sample size situations

Abstract

In this paper, we discuss the improvement of the generalization ability of Parzen classifiers, in small sample, high-dimensional setting. When the sizes of samples per class are much unequal, the performance of the Parzen classifier is further degraded. Also, in a high-dimensional space, the degradation becomes clear. In order to overcome this problem, we propose to use the Toeplitz estimator and bootstrap samples in designing Parzen classifiers. Experimental results show that these two techniques are very effective means for designing Parzen classifiers, particularly when the sizes of samples per class are much unequal, or when the number of features is large.