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Poster
Consistent Multilabel Classification
Oluwasanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #39
in Poster Session »

Multilabel classification is rapidly developing as an important aspect of modern predictive modeling, motivating study of its theoretical aspects. To this end, we propose a framework for constructing and analyzing multilabel classification metrics which reveals novel results on a parametric form for population optimal classifiers, and additional insight into the role of label correlations. In particular, we show that for multilabel metrics constructed as instance-, micro- and macro-averages, the population optimal classifier can be decomposed into binary classifiers based on the marginal instance-conditional distribution of each label, with a weak association between labels via the threshold. Thus, our analysis extends the state of the art from a few known multilabel classification metrics such as Hamming loss, to a general framework applicable to many of the classification metrics in common use. Based on the population-optimal classifier, we propose a computationally efficient and general-purpose plug-in classification algorithm, and prove its consistency with respect to the metric of interest. Empirical results on synthetic and benchmark datasets are supportive of our theoretical findings.

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Author Information

Oluwasanmi Koyejo (Stanford University)
Nagarajan Natarajan (UT Austin)
Pradeep Ravikumar (University of Texas at Austin)
Inderjit Dhillon (University of Texas at Austin)

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