Single-molecule experiments
offer a unique means to probe molecular
properties of individual molecules–yet they rest upon the successful
control of background noise and irrelevant signals. In single-molecule
transport studies, large amounts of data that probe a wide range of
physical and chemical behaviors are often generated. However, due
to the stochasticity of these experiments, a substantial fraction
of the data may consist of blank traces where no molecular signal
is evident. One-class (OC) classification is a machine learning technique
to identify a specific class in a data set that potentially consists
of a wide variety of classes. Here, we examine the utility of two
different types of OC classification models on four diverse data sets
from three different laboratories. Two of these data sets were measured
at cryogenic temperatures and two at room temperature. By training
the models solely on traces from a blank experiment, we demonstrate
the efficacy of OC classification as a powerful and reliable method
for filtering out blank traces from a molecular experiment in all
four data sets. On a labeled 4,4′-bipyridine data set measured
at 4.2 K, we achieve an accuracy of 96.9 ± 0.3 and an area under
the receiver operating characteristic curve of 99.5 ± 0.3 as
validated over a fivefold cross-validation. Given the wide range of
physical and chemical properties that can be probed in single-molecule
experiments, the successful application of OC classification to filter
out blank traces is a major step forward in our ability to understand
and manipulate molecular properties.