  The study of model bias and variance with respect to decision boundaries is
critically important in supervised classification. There is generally a
tradeoff between the two, as fine-tuning of the decision boundary of a
classification model to accommodate more boundary training samples (i.e.,
higher model complexity) may improve training accuracy (i.e., lower bias) but
hurt generalization against unseen data (i.e., higher variance). By focusing on
just classification boundary fine-tuning and model complexity, it is difficult
to reduce both bias and variance. To overcome this dilemma, we take a different
perspective and investigate a new approach to handle inaccuracy and uncertainty
in the training data labels, which are inevitable in many applications where
labels are conceptual and labeling is performed by human annotators. The
process of classification can be undermined by uncertainty in the labels of the
training data; extending a boundary to accommodate an inaccurately labeled
point will increase both bias and variance. Our novel method can reduce both
bias and variance by estimating the pointwise label uncertainty of the training
set and accordingly adjusting the training sample weights such that those
samples with high uncertainty are weighted down and those with low uncertainty
are weighted up. In this way, uncertain samples have a smaller contribution to
the objective function of the model's learning algorithm and exert less pull on
the decision boundary. In a real-world physical activity recognition case
study, the data presents many labeling challenges, and we show that this new
approach improves model performance and reduces model variance.
