295 research outputs found
Reliable Multi-label Classification: Prediction with Partial Abstention
In contrast to conventional (single-label) classification, the setting of
multilabel classification (MLC) allows an instance to belong to several classes
simultaneously. Thus, instead of selecting a single class label, predictions
take the form of a subset of all labels. In this paper, we study an extension
of the setting of MLC, in which the learner is allowed to partially abstain
from a prediction, that is, to deliver predictions on some but not necessarily
all class labels. We propose a formalization of MLC with abstention in terms of
a generalized loss minimization problem and present first results for the case
of the Hamming loss, rank loss, and F-measure, both theoretical and
experimental.Comment: 19 pages, 12 figure
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have been
proposed in the literature, the question of how to aggregate the predictions of
the individual members of the ensemble has received little attention so far. In
this paper, we introduce a formal framework of ensemble multilabel
classification, in which we distinguish two principal approaches: "predict then
combine" (PTC), where the ensemble members first make loss minimizing
predictions which are subsequently combined, and "combine then predict" (CTP),
which first aggregates information such as marginal label probabilities from
the individual ensemble members, and then derives a prediction from this
aggregation. While both approaches generalize voting techniques commonly used
for multilabel ensembles, they allow to explicitly take the target performance
measure into account. Therefore, concrete instantiations of CTP and PTC can be
tailored to concrete loss functions. Experimentally, we show that standard
voting techniques are indeed outperformed by suitable instantiations of CTP and
PTC, and provide some evidence that CTP performs well for decomposable loss
functions, whereas PTC is the better choice for non-decomposable losses.Comment: 14 pages, 2 figure
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