Locally nearest neighbor classifiers for pattern classification

Editing the nearest feature line classifier

“…Although the NFL method is successful in improving the classification ability of the NN approach, there is room for further improvements [12]. It has two main sources of errors, namely the interpolation and extrapolation inaccuracies.…”

“…Experiments have shown that CNN achieves enhanced performance compared to NN and comparable performance with NFL [11]. Another approach for reducing the computational cost is the nearest neighbor line (NNL) [12]. It uses the line through the nearest pair of samples from each class during the classification phase.…”

A New Finger Vein Recognition Method Based on the Difference Symmetric Local Graph Structure (DSLGS)

“…In the experiment, we use Nearest Neighbor Classification(1-NN) [15] to classify the finger vein images. The biggest advantage of this algorithm is effective and fast.…”

Neighbor Line-Based Locally Linear Embedding

“…the unseen data point to be classified, is far from the prototype points because in this case, unreliable extrapolated points may be queried for classifying the unseen data. In order to reduce the influence of this problem, the Nnl (Nearest Neighbor Line) method, which is a modified version of Nfl, was proposed, where only the neighbors of the query point instead of all the possible feature lines are used [13]. …”

“…For example, in the Smote algorithm [1], virtual samples of the minority class are generated such that the number of minority training samples is increased. Here the virtual samples are generated through interpolating between each minority class point and its k nearest neighboring minority class points, which looks somewhat like the interpolating scheme used in Nfl [8] and Nnl [13]. In fact, the Nfl and Nnl algorithms have implicitly utilized virtual samples since they use a virtual point instead of a real data point to help compute the distance between a data point and a class.…”