Leveraging cooperation for parallel multi‐objective feature selection in high‐dimensional EEG data

Abstract

SUMMARYBioinformatics applications frequently involve high-dimensional model building or classification problems that require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. Thus, feature selection has become an important issue on these applications. Moreover, several approaches for supervised and unsupervised feature selections as a multi-objective optimization problem have been recently proposed to cope with issues on performance evaluation of classifiers and models. As parallel processing constitutes an important tool to reach efficient approaches that make it possible to tackle complex problems within reasonable computing times, in this paper, alternatives for the cooperation of subpopulations in multi-objective evolutionary algorithms have been identified and classified, and several procedures have been implemented and evaluated on some synthetic and Brain-Computer Interface datasets. The results show different improvements achieved in the solution quality and speedups, depending on the cooperation alternative and dataset. We show alternatives that even provide superlinear speedups with only small reductions in the solution quality, besides another cooperation alternative that improves the quality of the solutions with speedups similar to, or only slightly higher than, the speedup obtained by the parallel fitness evaluation in a master-worker implementation (the alternative used as reference that behaves as the corresponding sequential multi-objective approach).

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications

“…To summarize, for person identification within optimization algorithms, two schemes have been observed: first, optimization scheme based on single objective criteria, mainly channel selection; second, optimization scheme based multiobjective criteria such as channel selection, EEG noise, and classifiers hyperparameters. Bioinformatics applications frequently involve classification problems that require improving the learning accuracy [ 33 ]. According to [ 32 ], certain aspects need to be analyzed and improved before reaching an industrial level application of new biometric systems.…”

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off

“…In addition to model accuracy, emphasis is also placed on computational efficiency by measuring time and energy consumption. More in-depth studies on the smart use of available computing devices applied to our datasets can be found in [36,37].…”

Optimization of problems with multiple objectives using the multi-verse optimization algorithm

“…In addition, another important aspect of multi-objective optimization algorithms is the execution time to determine an accurate estimation of true Pareto optimal solutions. Parallel multiobjective optimization algorithms are the best solutions to decrease the run time [20,32].…”