Abstract
Extracting proper features is crucial to the performance of a pattern recognition system. Since the goal of a pattern recognition system is to recognize a pattern correctly, a natural measure of “goodness” of extracted features is the probability of classification error. However, popular feature extraction techniques like principal component analysis (PCA), Fisher linear discriminant analysis (FLD), and independent component analysis (ICA) extract features that are not directly related to the classification accuracy. In this paper, we present two linear discriminant analysis algorithms (LDA) whose criterion functions are directly based on minimum probability of classification error, or the Bayes error. We term these two linear discriminants as recursive Bayesian linear discriminant I (RBLD-I) and recursive Bayesian linear discriminant II (RBLD-II). Experiments on databases from UCI Machine Learning Repository show that the two novel linear discriminants achieve superior classification performance over recursive FLD (RFLD).
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Huang, D., Xiang, C. (2007). Recursive Bayesian Linear Discriminant for Classification. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_119
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DOI: https://doi.org/10.1007/978-3-540-72393-6_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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