32 research outputs found
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and
important problem since the boom of big data. The main challenge lies in the
exponential label space which involves possible label sets especially
when the label dimension is huge, e.g., in millions for Wikipedia labels.
This paper is motivated to better explore the label space by originally
establishing an explicit label graph. In the meanwhile, deep learning has been
widely studied and used in various classification problems including
multi-label classification, however it has not been properly introduced to XML,
where the label space can be as large as in millions. In this paper, we propose
a practical deep embedding method for extreme multi-label classification, which
harvests the ideas of non-linear embedding and graph priors-based label space
modeling simultaneously. Extensive experiments on public datasets for XML show
that our method performs competitive against state-of-the-art result
Nearest Labelset Using Double Distances for Multi-label Classification
Multi-label classification is a type of supervised learning where an instance
may belong to multiple labels simultaneously. Predicting each label
independently has been criticized for not exploiting any correlation between
labels. In this paper we propose a novel approach, Nearest Labelset using
Double Distances (NLDD), that predicts the labelset observed in the training
data that minimizes a weighted sum of the distances in both the feature space
and the label space to the new instance. The weights specify the relative
tradeoff between the two distances. The weights are estimated from a binomial
regression of the number of misclassified labels as a function of the two
distances. Model parameters are estimated by maximum likelihood. NLDD only
considers labelsets observed in the training data, thus implicitly taking into
account label dependencies. Experiments on benchmark multi-label data sets show
that the proposed method on average outperforms other well-known approaches in
terms of Hamming loss, 0/1 loss, and multi-label accuracy and ranks second
after ECC on the F-measure