Title: Regularizing Model Complexity and Label Structure for Multi-Label Text Classification,
Abstract: Multi-label text classification is a popular machine learning task where each
document is assigned with multiple relevant labels. This task is challenging
due to high dimensional features and correlated labels. Multi-label text
classifiers need to be carefully regularized to prevent the severe over-fitting
in the high dimensional space, and also need to take into account label
dependencies in order to make accurate predictions under uncertainty. We
demonstrate significant and practical improvement by carefully regularizing the
model complexity during training phase, and also regularizing the label search
space during prediction phase. Specifically, we regularize the classifier
training using Elastic-net (L1+L2) penalty for reducing model complexity/size,
and employ early stopping to prevent overfitting. At prediction time, we apply
support inference to restrict the search space to label sets encountered in the
training set, and F-optimizer GFM to make optimal predictions for the F1
metric. We show that although support inference only provides density
estimations on existing label combinations, when combined with GFM predictor,
the algorithm can output unseen label combinations. Taken collectively, our
experiments show state of the art results on many benchmark datasets. Beyond
performance and practical contributions, we make some interesting observations.
Contrary to the prior belief, which deems support inference as purely an
approximate inference procedure, we show that support inference acts as a
strong regularizer on the label prediction structure. It allows the classifier
to take into account label dependencies during prediction even if the
classifiers had not modeled any label dependencies during training.