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

arXiv: https://arxiv.org/abs/2212.04382 · HTML: https://mlsanity.com/paper/2212.04382/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Measuring Quality of DNA Sequence Data via Degradation](https://mlsanity.com/paper/2112.13111/) (2021)
- [Application of Markov Structure of Genomes to Outlier Identification and Read Classification](https://mlsanity.com/paper/2112.13117/) (2021)

## Built on by (most cited first, 1 of 1)

- [Effects of Training Data Quality on Classifier Performance](https://mlsanity.com/paper/2602.21462/) (2026)

## Abstract (arXiv)

For a Bayes classifier whose input space is a graph, we study the structure of the boundary, which comprises those points for which at least one neighbor is classified differently. The scientific setting is assignment of DNA reads produced by next generations sequencers to candidate source genomes. We show that the boundary is both large and complicated in structure. A new measure of uncertainty, Neighbor Similarity, which compares the classifier result for an input point to the distribution of results for its neighbors, not only tracks two inherent uncertainty measures for the Bayes classifier, but also can be implemented for classifiers without inherent measures of uncertainty.