277 research outputs found
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Neural networks have recently been proposed for multi-label classification
because they are able to capture and model label dependencies in the output
layer. In this work, we investigate limitations of BP-MLL, a neural network
(NN) architecture that aims at minimizing pairwise ranking error. Instead, we
propose to use a comparably simple NN approach with recently proposed learning
techniques for large-scale multi-label text classification tasks. In
particular, we show that BP-MLL's ranking loss minimization can be efficiently
and effectively replaced with the commonly used cross entropy error function,
and demonstrate that several advances in neural network training that have been
developed in the realm of deep learning can be effectively employed in this
setting. Our experimental results show that simple NN models equipped with
advanced techniques such as rectified linear units, dropout, and AdaGrad
perform as well as or even outperform state-of-the-art approaches on six
large-scale textual datasets with diverse characteristics.Comment: 16 pages, 4 figures, submitted to ECML 201
A Contextual Bandit Bake-off
Contextual bandit algorithms are essential for solving many real-world
interactive machine learning problems. Despite multiple recent successes on
statistically and computationally efficient methods, the practical behavior of
these algorithms is still poorly understood. We leverage the availability of
large numbers of supervised learning datasets to empirically evaluate
contextual bandit algorithms, focusing on practical methods that learn by
relying on optimization oracles from supervised learning. We find that a recent
method (Foster et al., 2018) using optimism under uncertainty works the best
overall. A surprisingly close second is a simple greedy baseline that only
explores implicitly through the diversity of contexts, followed by a variant of
Online Cover (Agarwal et al., 2014) which tends to be more conservative but
robust to problem specification by design. Along the way, we also evaluate
various components of contextual bandit algorithm design such as loss
estimators. Overall, this is a thorough study and review of contextual bandit
methodology
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
Efficient multi-label classification for evolving data streams
Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory.
This paper proposes a new experimental framework for studying multi-label evolving stream classification, and new efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. We present a Multi-label Hoeffding Tree with multilabel classifiers at the leaves as a base classifier. We obtain fast and accurate methods, that are well suited for this challenging multi-label classification streaming task. Using the new experimental framework, we test our methodology by performing an evaluation study on synthetic and real-world datasets. In comparison to well-known batch multi-label methods, we obtain encouraging results
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