A novel classifier based on shortest feature line segment

A new approach called shortest feature line segment (SFLS) is proposed to implement pattern classification in this paper, which can retain the ideas and advantages of nearest feature line (NFL) and at the same time can counteract the drawbacks of NFL. The proposed SFLS uses the length of the feature line segment satisfying given geometric relation with query point instead of the perpendicular distance defined in NFL. SFLS has clear geometric–theoretic foundation and is relatively simple. Experimental results on some artificial datasets and real-world datasets are provided, together with the comparisons between SFLS and other neighborhood-based classification methods, including nearest neighbor (NN), k-NN, NFL and some refined NFL methods, etc. It can be concluded that SFLS is a simple yet effective classification approach.

Research highlights
► The proposed SFLS is an improvement of the NFL and other NFL-rectified methods.
► SFLS has clear geometric-theoretic foundation.
► On an experimental basis, SFLS has good classification performance.