Abstract
K-Nearest-Neighbor (KNN) has been widely used in classification problems. However, there exist three main problems confronting KNN according to our observation: 1) KNN’s accuracy is degraded by a simple vote; 2) KNN’s accuracy is typically sensitive to the value of K; 3) KNN’s accuracy may be dominated by some irrelevant attributes. In this paper, we presented an improved algorithm called Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted (DKNAW) . We experimentally tested its accuracy, using the whole 36 UCI data sets selected by Weka[1], and compared it to NB, KNN, KNNDW, and LWNB[2]. The experimental results show that DKNAW significantly outperforms NB, KNN, and KNNDW and slightly outperforms LWNB.
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Jiang, L., Zhang, H., Cai, Z. (2006). Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_41
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DOI: https://doi.org/10.1007/11881599_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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