F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets

Maxime Gasse 1 Alex Aussem 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We discuss a method to improve the exact F-measure max-imization 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 O(m^2) operations [10]. In the general case, m^2 + 1 parameters are required by GFM, to solve the problem in O(m^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.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01425528
Contributor : Maxime Gasse <>
Submitted on : Tuesday, January 3, 2017 - 3:56:36 PM
Last modification on : Thursday, November 21, 2019 - 2:02:03 AM
Long-term archiving on: Tuesday, April 4, 2017 - 2:39:59 PM

File

ECML_2016 (1).pdf
Files produced by the author(s)

Identifiers

Citation

Maxime Gasse, Alex Aussem. F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2016, Riva del garda, Italy. pp.619 - 631, ⟨10.1007/978-3-319-46128-1_39⟩. ⟨hal-01425528⟩

Share

Metrics

Record views

388

Files downloads

413