Abstract
In this paper we present a way to reduce the computational cost of k-NN classifiers without losing classification power. Hierarchical or multistage classifiers have been built with this purpose. These classifiers are designed putting incrementally trained classifiers into a hierarchy and using rejection techniques in all the levels of the hierarchy apart from the last. Results are presented for different benchmark data sets: some standard data sets taken from the UCI Repository and the Statlog Project, and NIST Special Databases (digits and upper-case and lower-case letters). In all the cases a computational cost reduction is obtained maintaining the recognition rate of the best individual classifier obtained.
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Keywords
- Recognition Rate
- Near Neighbor
- Good Recognition Rate
- Multistage Classifier
- Handwritten Digit Recognition
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Soraluze, I., Rodriguez, C., Boto, F., Cortes, A. (2003). Fast Multistage Algorithm for K-NN Classifiers. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds) Progress in Pattern Recognition, Speech and Image Analysis. CIARP 2003. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24586-5_55
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DOI: https://doi.org/10.1007/978-3-540-24586-5_55
Publisher Name: Springer, Berlin, Heidelberg
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