Affiliations: Department of Computer Engineering, Eastern Mediterranean University Famagusta, Northern Cyprus, Turkey
Correspondence:
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Corresponding author: Hakan Altınçay, Department of Computer Engineering, Eastern Mediterranean University Famagusta, Northern Cyprus, Turkey. Tel.: +90 392 6302842; Fax: +90 392 3650711; E-mail:hakan.altincay@emu.edu.tr
Abstract: Two major drawbacks in nearest feature line classifier are the extrapolation and interpolation inaccuracies. The former
can easily be counteracted by considering segments instead of lines. However, the solution of the latter problem is
more challenging. Recently developed techniques tackle with this drawback by selecting a subset of the feature line
segments either during training or testing. In this study, a novel scheme that is based on editing the feature line
segments is proposed. It involves two major strategies, namely generalization-based elimination and
representation-based elimination. In generalization-based elimination, the benefit and cost of discarding each feature
line segment are compared and the segments that contribute to the classification error are discarded. For the
implementation of representation-based elimination, a measure of intersection is defined and the longer of each pair of
intersecting segments are discarded. Moreover, the distances of the feature line segments to the samples of other
classes are also investigated to discard inconsistent segments. The proposed steps are evaluated by applying them in
different orders. Experimental results have shown that the proposed approach provides better accuracies when compared
to two recently developed extensions of the nearest feature line approach, namely shortest feature line segment and
rectified nearest feature line segment.
