Noise Effects on Feature Mining Non-Parametric Supervised Feature Extraction Techniques

Abstract

In this paper two famous and commonly used feature mining non-parametric supervised feature extraction techniques (NSFETs) called Non-parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE) are analyzed to see their efficiency in the presence of noise. In particularly these feature extraction techniques are used in classification as they give better classification accuracy. This study reveals that NSFETs are very sensitive to noise because of which the number of features increases and we get low classification accuracy. In order to see the behavior of NSFETs, spatial and spectral information from hyperspectral image classification is used. The experimental results show that in the presence of noise, spectral information is much more effected than the spatial information when features are extracted using the NSFETs. It is also examined that NWFE is more affected by noise than DBFE. The linear filtering technique is used just before the classifier in order to mitigate the noise effects in NSFETs. Using linear filtering just before the classifier does improve the final classification accuracy but with high number of spatial and spectral features. This does not satisfy the one of the main purpose of feature extraction and that is feature reduction.