Bootstrapping search margin-based nearest neighbor method for qualitative spectroscopic analysis.

Qualitative spectroscopic analysis depends in one way or another on comparing spectra of the specimens to be identified with spectra of "known" or "standard" samples. The k-nearest neighbor (k-NN) method is one of the oldest and simplest techniques for performing such comparisons. In this study, we present a new k-NN algorithm for qualitative spectroscopic analysis, which we refer to as the bootstrapping search margin-based nearest neighbor (BSMNN) method. This method consists of two phases. In the first phase, we attempt to find a feature space in which samples with different labels produce large margins, such that the classification has high confidence, by maximizing a margin-based objective function using a weighted bootstrap sampling search strategy. In the subsequent classification phase, we compute local Euclidean distances between different samples under the feature space. A new instance x is classified by the label of its nearest neighbor. Six widely used vibrational spectroscopic data sets were used to validate the performance of the BSMNN method. The results showed that, despite its simplicity, BSMNN yields better results compared with commonly used k-NN algorithms including Relief, neighborhood components analysis, neighborhood component feature selection, and large margin nearest neighbor. Furthermore, BSMNN can be used to identify important spectral regions. It is worth mentioning that the margin-based objective function used in BSMNN is proposed for the first time for measuring the quality of features. Although in this study the margin-based objective function is focused on k-NN classification, it also can be used for other distance-based classifiers, such as soft independent modeling of class analogies and least squares support vector machine.