Rectified nearest feature line segment for pattern classification

Authors:
Hao Du;Yan Qiu Chen
Affiliations:
Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai 200433, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai 200433, China
Venue:
Pattern Recognition
Year:
2007

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Abstract

This paper points out and analyzes the advantages and drawbacks of the nearest feature line (NFL) classifier. To overcome the shortcomings, a new feature subspace with two simple and effective improvements is built to represent each class. The proposed method, termed rectified nearest feature line segment (RNFLS), is shown to possess a novel property of concentration as a result of the added line segments (features), which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex tasks such as the two-spiral distribution, which the original NFL cannot deal with properly. Finally, experimental comparisons with NFL, NN(nearest neighbor), k-NN and NNL (nearest neighbor line) using both artificial and real-world data-sets demonstrate that RNFLS offers the best performance.