Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection

• 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.

Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain–Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile.