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6

PKDD
2015
Springer

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Study on Meta-Learning Approach Application in Rank Aggregation Algorithm Selection

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Study on Meta-Learning Approach Application in Rank Aggregation Algorithm Selection

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Rank aggregation is an important task in many areas, nevertheless, none of rank aggregation algorithms is best for all cases. The main goal of this work is to develop a method, which for a given rank list finds the best rank aggregation algorithm with respect to a certain optimality criterion. Two approaches based on meta-feature description are proposed and one of them shows promising results.

Alexey Zabashta, Ivan Smetannikov, Andrey Filchenk

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Added	16 Apr 2016
Updated	16 Apr 2016
Type	Journal
Year	2015
Where	PKDD
Authors	Alexey Zabashta, Ivan Smetannikov, Andrey Filchenkov

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