This paper presents a Min-Max modular k-nearest neighbor (M 3-k-NN) classification method for massively parallel text categorization. The basic idea behind the method is to decompose a large-scale text categorization problem into a number of smaller two-class subproblems and combine all of the individual modular k-NN classifiers trained on the smaller two-class subproblems into an M3-k-NN classifier. Our experiments in text categorization demonstrate that M -k-NN is much faster than conventional k-NN, and meanwhile the classification accuracy of M3-k-NN is slightly better than that of the conventional k-NN. In practical, M3-k-NN has intimate relationship with high order k-NN algorithm; therefore, in theoretical sense, the reliability of M3-k-NN has been supported to some extend. © Springer-Verlag 2004.
CITATION STYLE
Zhao, H., & Lu, B. L. (2004). A modular k-nearest neighbor classification method for massively parallel text categorization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3314, 867–872. https://doi.org/10.1007/978-3-540-30497-5_134
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