J. Scott Olsson
ABSTRACT
We consider the relationship between training set size and the parameter k for the k-Nearest Neighbors (kNN) classifier. When few examples are available, we observe that accuracy is sensitive to k and that best k tends to increase with training size. We explore the subsequent risk that k tuned on partitions will be suboptimal after aggregation and re-training. This risk is found to be most severe when little data is available. For larger training sizes, accuracy becomes increasingly stable with respect to k and the risk decreases.
- D. D. Lewis, et al., RCV1: A New Benchmark Collection for Text Categorization Research. J. Mach. Learn. Res., 5, 2004. Google Scholar
Digital Library
- Y. Yang. An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval, 1, 1999. Google ScholarDigital Library
- Y. Yang, et al., A Scalability Analysis of Classifiers in Text Categorization. In SIGIR, 2003. Google ScholarDigital Library
Index Terms
- An analysis of the coupling between training set and neighborhood sizes for the kNN classifier
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