On Using Nearly-Independent Feature Families for High Precision and Confidence

Often we require classification at a very high precision level, such
as 99%. We report that when very different sources of evidence such as
text, audio, and video features are available, combining the outputs
of base classifiers trained on each feature type separately, aka late
fusion, can substantially increase the recall of the combination at
high precisions, compared to the performance of a single classifier
trained on all the feature types i.e., early fusion, or compared to
the individual base classifiers. We show how the probability of a
joint false-positive mistake can be upper bounded by the product of
individual probabilities of conditional false-positive mistakes, by
identifying a simple key criterion that needs to hold. This provides
an explanation for the high precision phenomenon, and motivates
referring to such feature families as (nearly) independent. We assess
the relevant factors for achieving high precision empirically, and
explore combination techniques informed by the analysis. We compare a
number of early and late fusion methods, and observe that classifier
combination via late fusion can more than double the recall at high
precision.

• Machine Perception

• Machine Intelligence