Statistics > Machine Learning
[Submitted on 8 Dec 2022 (v1), last revised 9 Feb 2024 (this version, v2)]
Title:Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier
View PDF HTML (experimental)Abstract:Whether based on models, training data or a combination, classifiers place (possibly complex) input data into one of a relatively small number of output categories. In this paper, we study the structure of the boundary--those points for which a neighbor is classified differently--in the context of an input space that is a graph, so that there is a concept of neighboring inputs, The scientific setting is a model-based naive Bayes classifier for DNA reads produced by Next Generation Sequencers. We show that the boundary is both large and complicated in structure. We create a new measure of uncertainty, called Neighbor Similarity, that compares the result for a point to the distribution of results for its neighbors. This measure not only tracks two inherent uncertainty measures for the Bayes classifier, but also can be implemented, at a computational cost, for classifiers without inherent measures of uncertainty.
Submission history
From: Alan Karr [view email][v1] Thu, 8 Dec 2022 16:23:42 UTC (1,969 KB)
[v2] Fri, 9 Feb 2024 16:48:37 UTC (3,349 KB)
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