56,033 research outputs found
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models
in Computer Vision and Machine Learning. The Springer Series on Challenges in
Machine Learning. Springer (2018). See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further
informatio
Multilabel Classification with R Package mlr
We implemented several multilabel classification algorithms in the machine
learning package mlr. The implemented methods are binary relevance, classifier
chains, nested stacking, dependent binary relevance and stacking, which can be
used with any base learner that is accessible in mlr. Moreover, there is access
to the multilabel classification versions of randomForestSRC and rFerns. All
these methods can be easily compared by different implemented multilabel
performance measures and resampling methods in the standardized mlr framework.
In a benchmark experiment with several multilabel datasets, the performance of
the different methods is evaluated.Comment: 18 pages, 2 figures, to be published in R Journal; reference
correcte
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