Center-based nearest neighbor classifier

摘要

In this paper, a novel center-based nearest neighbor (CNN) classifier is proposed to deal with the pattern classification problems. Unlike nearest feature line (NFL) method, CNN considers the line passing through a sample point with known label and the center of the sample class. This line is called the center-based line (CL). These lines seem to have more capacity of representation for sample classes than the original samples and thus can capture more information. Similar to NFL, CNN is based on the nearest distance from an unknown sample point to a certain CL for classification. As a result, the computation time of CNN can be shortened dramatically with less accuracy decrease when compared with NFL. The performance of CNN is demonstrated in one simulation experiment from computational biology and high classification accuracy has been achieved in the leave-one-out test. The comparisons with nearest neighbor (NN) classifier and NFL classifier indicate that this novel classifier achieves competitive performance.