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.
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References
Li, S.Z., Lu, J.W.: Face recognition using the nearest feature line method. IEEE Trans. Neural Networks 10, 439–443 (1999)
Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 24, 1644–1649 (2002)
Li, S.Z.: Content-based audio classification and retrieval using the nearest feature line method. IEEE Trans. Speech Audio Processing 8, 619–625 (2000)
Li, S.Z., Chan, K.L., Wang, C.L.: Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE Trans. Pattern Anal. Machine Intell. 22, 1335–1339 (2000)
Chen, K., Wu, T.Y., Zhang, H.J.: On the use of nearest feature line for speaker identification. Pattern Recognition Letters 23, 1735–1746 (2002)
Chen, J.H., Chen, C.S.: Object recognition based on image sequences by using inter-feature-line consistencies. Pattern Recognition 37, 1913–1923 (2004)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inform. Theory 13, 21–27 (1967)
Zheng, W.M., Zhao, L., Zou, C.R.: Locally nearest neighbor classifiers for pattern classification. Pattern Recognition 37, 1307–1309 (2004)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. [Online] (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
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© 2005 Springer-Verlag Berlin Heidelberg
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Du, H., Chen, Y.Q. (2005). Pattern Classification Using Rectified Nearest Feature Line Segment. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_11
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DOI: https://doi.org/10.1007/11540007_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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