Editing the Nearest Feature Line Classifier

ABSTRACT: The main drawbacks in Nearest Feature Line classifier are the extrapolation and interpolation inaccuracies. The former can easily be counteracted by considering segment rather than 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 framework is developed that involves a discriminative component. The proposed approach is based on editing the feature line segments. It involves three major steps namely, error-based deletion, intersection-based deletion and pruning. The first step compares the benefit and cost of deleting each feature line segment and deletes those that contribute more to the classification error. For the implementation of the second step, a novel measure of intersection is defined and used for line segments in high dimensions to delete the longest of two intersecting segments. The pruning step re-evaluates the retained segments by considering their distances from the samples belonging to the other classes. The proposed approach is evaluated on fifteen real datasets from different domains. Experimental results have shown that the proposed scheme achieves better accuracies on majority of these datasets compared to two recently developed extensions of the nearest feature line approach, namely the rectified nearest feature line segment and shortest feature line segment on majority of these datasets. Keywords: Pattern classification; nearest feature line; line segment editing; interpolation inaccuracy; extrapolation inaccuracy. ……………………………………………………………………………………………………………………………………………………………………………………………………………………