🤖 AI Summary
This paper addresses the challenge of vast and structurally complex decision boundaries in DNA read mapping to reference genomes in next-generation sequencing (NGS). It investigates the boundary properties of naïve Bayes classifiers under graph-structured input spaces. To this end, the authors propose “neighborhood similarity” — a novel uncertainty measure that is both theoretically interpretable and universally computable, overcoming the reliance of conventional Bayesian confidence on model outputs. Leveraging graph-model-driven boundary analysis, neighborhood distribution statistics, and uncertainty quantification, the study reveals the high-dimensional complexity of decision boundaries and proves that the proposed measure simultaneously captures intrinsic Bayesian uncertainty. Moreover, it seamlessly extends to black-box classifiers lacking built-in confidence mechanisms. Empirically, neighborhood similarity significantly enhances classification interpretability and robustness, offering a principled framework for uncertainty-aware read mapping in NGS applications.
📝 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.