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

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.

• Type: Journal
• Year: 2015
• Where: PKDD
• Authors: Alexey Zabashta, Ivan Smetannikov, Andrey Filchenkov
• Keywords: Data Mining, PKDD 2015