Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection
作者：
Highlights：
• Multiobjective unsupervised feature selection with many decision variables is tackled.
• EEG signals for Brain–Computer Interface (BCI) applications are used as benchmarks.
• Cooperative evolutionary algorithms for multiobjective optimization are given.
• Parallel implementations obtain quality results in terms of hypervolume and speedup.
• Superlinear speedups are justified by adjusting models to experimental results.
摘要
•Multiobjective unsupervised feature selection with many decision variables is tackled.•EEG signals for Brain–Computer Interface (BCI) applications are used as benchmarks.•Cooperative evolutionary algorithms for multiobjective optimization are given.•Parallel implementations obtain quality results in terms of hypervolume and speedup.•Superlinear speedups are justified by adjusting models to experimental results.
论文关键词：Feature selection,High-dimensional data,Multi-objective clustering,Parallel evolutionary algorithms,Speedup models,Unsupervised classification
论文评审过程：Available online 4 February 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.01.061
