Abstract
This paper propose a method which improves performance of kNN based text classification by using well estimated parameters. Some variants of the kNN method with different decision functions, k values, and feature sets are proposed and evaluated to find out adequate parameters. Our experimental results show that kNN method with carefully chosen parameters are very significant in improving the performance and reducing size of feature set. We carefully conclude that it is very worthy of tuning parameters of kNN method to increase performance rather than having hard time in developing a new learning method.
This Work was Supported by Hanshin University Research Grant in (2004).
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© 2004 Springer-Verlag Berlin Heidelberg
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Lim, H.S. (2004). Improving kNN Based Text Classification with Well Estimated Parameters. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_79
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DOI: https://doi.org/10.1007/978-3-540-30499-9_79
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