sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification
This work addresses the challenge of optimizing for multilabel metrics in non-trivial classification problems, though it appears incremental as it builds on existing surrogate loss concepts.
The authors tackled the problem of multilabel classification by proposing a smooth surrogate loss function, sigmoidF1, which approximates the F1 score and outperformed other loss functions on one text and two image datasets across several metrics.
Multiclass multilabel classification is the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of the multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy, logistic, etc.). Multilabel classification reductions do not accommodate for the prediction of varying numbers of labels per example and the underlying losses are distant estimates of the performance metrics. We propose a loss function, sigmoidF1, which is an approximation of the F1 score that (1) is smooth and tractable for stochastic gradient descent, (2) naturally approximates a multilabel metric, and (3) estimates label propensities and label counts. We show that any confusion matrix metric can be formulated with a smooth surrogate. We evaluate the proposed loss function on text and image datasets, and with a variety of metrics, to account for the complexity of multilabel classification evaluation. sigmoidF1 outperforms other loss functions on one text and two image datasets and several metrics. These results show the effectiveness of using inference-time metrics as loss functions for non-trivial classification problems like multilabel classification.