@inbook{67c01b5e95074f909de0940a8bea6d68,
title = "Improving kNN based text classification with well estimated parameters",
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.",
author = "Lim, {Heui Seok}",
year = "2004",
doi = "10.1007/978-3-540-30499-9_79",
language = "English",
isbn = "3540239316",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "516--523",
editor = "Pal, {Nikhil R.} and Srimanta Pal and Nikola Kasabov and Mudi, {Rajani K.} and Parui, {Swapan K.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}