Aligning Multiclass Neural Network Classifier Criterion with Task Performance via F_β-Score
CoRR(2024)
摘要
Multiclass neural network classifiers are typically trained using
cross-entropy loss. Following training, the performance of this same neural
network is evaluated using an application-specific metric based on the
multiclass confusion matrix, such as the Macro F_β-Score. It is
questionable whether the use of cross-entropy will yield a classifier that
aligns with the intended application-specific performance criteria,
particularly in scenarios where there is a need to emphasize one aspect of
classifier performance. For example, if greater precision is preferred over
recall, the β value in the F_β evaluation metric can be adjusted
accordingly, but the cross-entropy objective remains unaware of this preference
during training. We propose a method that addresses this training-evaluation
gap for multiclass neural network classifiers such that users can train these
models informed by the desired final F_β-Score. Following prior work in
binary classification, we utilize the concepts of the soft-set confusion
matrices and a piecewise-linear approximation of the Heaviside step function.
Our method extends the 2 × 2 binary soft-set confusion matrix to a
multiclass d × d confusion matrix and proposes dynamic adaptation of the
threshold value τ, which parameterizes the piecewise-linear Heaviside
approximation during run-time. We present a theoretical analysis that shows
that our method can be used to optimize for a soft-set based approximation of
Macro-F_β that is a consistent estimator of Macro-F_β, and our
extensive experiments show the practical effectiveness of our approach.
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