Random Forest for Dissimilarity-based Multi-view Learning
- URL: http://arxiv.org/abs/2007.08377v1
- Date: Thu, 16 Jul 2020 14:52:52 GMT
- Title: Random Forest for Dissimilarity-based Multi-view Learning
- Authors: Simon Bernard, Hongliu Cao, Robert Sabourin, Laurent Heutte
- Abstract summary: We show that the Random Forest proximity measure can be used to build the dissimilarity representations.
We then propose a Dynamic View Selection method to better combine the view-specific dissimilarity representations.
- Score: 8.185807285320553
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many classification problems are naturally multi-view in the sense their data
are described through multiple heterogeneous descriptions. For such tasks,
dissimilarity strategies are effective ways to make the different descriptions
comparable and to easily merge them, by (i) building intermediate dissimilarity
representations for each view and (ii) fusing these representations by
averaging the dissimilarities over the views. In this work, we show that the
Random Forest proximity measure can be used to build the dissimilarity
representations, since this measure reflects similarities between features but
also class membership. We then propose a Dynamic View Selection method to
better combine the view-specific dissimilarity representations. This allows to
take a decision, on each instance to predict, with only the most relevant views
for that instance. Experiments are conducted on several real-world multi-view
datasets, and show that the Dynamic View Selection offers a significant
improvement in performance compared to the simple average combination and two
state-of-the-art static view combinations.
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