Pattern Classification Using Rectified Nearest Feature Line Segment

Abstract

This paper proposes a new classification method termed Rectified Nearest Feature Line Segment (RNFLS). It overcomes the drawbacks of the original Nearest Feature Line (NFL) classifier and possesses a novel property that centralizes the probability density of the initial sample distribution, which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex problems such as two-spirals, which the original NFL cannot deal with properly. Experimental comparisons with NFL, NN(Nearest Neighbor), k-NN and NNL (Nearest Neighbor Line) using artificial and real-world datasets demonstrate that RNFLS offers the best performance.