Name
Abstract | ||
|---|---|---|
Rank aggregation is the important task in many areas, and different rank aggregation algorithms are created to find optimal rank. Nevertheless, none of these algorithms is the best for all cases. The main goal of this work is to develop a method, which for each rank list defines, which rank aggregation algorithm is the best for this rank list. Canberra distance is used as a metric for determining the optimal ranking. Three approaches are proposed in this paper and one of them has shown promising result. Also we discuss, how this approach can be applied to learn filtering feature selection algorithm ensemble. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-41920-6_56 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Meta-learning,Rank aggregation,Ensemble learning,Feature selection | Canberra distance,Feature selection,Pattern recognition,Ranking,Computer science,Filter (signal processing),Artificial intelligence,Algorithm Selection,Ensemble learning,Machine learning | Conference |
Volume | ISSN | Citations |
9729 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexey Zabashta | 1 | 1 | 0.35 |
| Ivan Smetannikov | 2 | 1 | 1.37 |
| Andrey Filchenkov | 3 | 46 | 15.80 |