Name
Title | ||
|---|---|---|
Many-objective optimization of feature selection based on two-level particle cooperation. |
Abstract | ||
|---|---|---|
•Feature selection (FS) of high-dimensional data is reformulated as a many-objective optimization problem (MaOP) , consisting of three objectives to be minimized simultaneously.•To solve the formulated problem, we developed a PSO-based algorithm to search for the Pareto optimal solutions with two-level particle cooperation under the MOEA/D framework.•We made a systematical comparison between these the proposed methods and some state-of-the-art single objective and other MaOP FS approaches and the results demonstrate the efficacy of our proposed methods both in classification accuracy on the test data and the size of the feature subset. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2020.05.004 | Information Sciences |
Keywords | DocType | Volume |
Feature selection,Particle swarm optimization,Many-objective evolutionary algorithm,ReliefF,Pareto optimal solutions | Journal | 532 |
ISSN | Citations | PageRank |
0020-0255 | 2 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhou | 1 | 58 | 4.86 |
| Junhao Kang | 2 | 10 | 1.15 |
| Hainan Guo | 3 | 4 | 1.44 |