Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier

• URL: http://arxiv.org/abs/2212.04382v2
• Date: Fri, 9 Feb 2024 16:48:37 GMT
• Title: Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier
• Authors: Alan F. Karr, Zac Bowen, Adam A. Porter
• Abstract summary: 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.
• Score: 1.1485218363676564
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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