Component-Wise Boosting of Targets for Multi-Output Prediction
Journal
arXiv:1904.03943
Type
journal article
Date Issued
2019
Author(s)
Abstract
Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In this paper, we introduce an algorithm that uses the problem transformation method for multi-output prediction, while simultaneously learning the dependencies between target variables in a sparse and interpretable manner. In a first step, predictions are obtained for each target individually. Target dependencies are then learned via a component-wise boosting approach. We compare our new method with similar approaches in a benchmark using multi-label, multivariate regression and mixed-type datasets.
Language
English
Keywords
Multi-output prediction
Target dependencies
Component- wise boosting
Label correlations
Problem transformation
Multi-label classification
Multivariate regression
Refereed
No
Publisher
arXiv
Start page
1
End page
16
Official URL
Subject(s)
Division(s)
Eprints ID
264561
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Name
1904.03943.pdf
Size
374.37 KB
Format
Adobe PDF
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