Using ensembles for problems with characterizable changes in data distribution: A case study on quantification
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Our hypothesis: ensembles are well suited for problems with distribution changes.

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If those changes are characterizable, ensembles can be designed to tackle them.

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Idea: to generate different samples based on the expected distribution changes.

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Case study: we present ensembles versions of two binary quantification algorithms.

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Ensembles outperform original counterpart algorithms using trivial aggregation rules.

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