Title | ||
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F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. |
Abstract | ||
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We discuss a method to improve the exact F-measure maximization algorithm called GFM, proposed in [2] for multi-label classification, assuming the label set can be partitioned into conditionally independent subsets given the input features. If the labels were all independent, the estimation of only m parameters m denoting the number of labels would suffice to derive Bayes-optimal predictions in $$Om^2$$ operations [10]. In the general case, $$m^2 + 1$$ parameters are required by GFM, to solve the problem in $$Om^3$$ operations. In this work, we show that the number of parameters can be reduced further to $$m^2/n$$, in the best case, assuming the label set can be partitioned into n conditionally independent subsets. As this label partition needs to be estimated from the data beforehand, we use first the procedure proposed in [4] that finds such partition and then infer the required parameters locally in each label subset. The latter are aggregated and serve as input to GFM to form the Bayes-optimal prediction. We show on a synthetic experiment that the reduction in the number of parameters brings about significant benefits in terms of performance. The data and software related to this paper are available at https://github.com/gasse/fgfm-toy. |
Year | DOI | Venue |
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2016 | 10.1007/978-3-319-46128-1_39 | ECML/PKDD |
DocType | Volume | Citations |
Conference | abs/1604.07759 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
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Maxime Gasse | 1 | 22 | 4.87 |
Alex Aussem | 2 | 254 | 30.02 |