Feature selection in high-dimensional EEG data by parallel multi-objective optimization

Kimovski, Dragi; Ortega, Julio; Ortíz, Andrés; Baños, Raúl

doi:10.1109/cluster.2014.6968782

Cited by 9 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, clustering social media data streams are used as a tested application domain. Recently, the [62] presented a parallel implementation of a multi-objective feature selection. The algorithm makes possible use of high dimensional datasets when there are much more features than data items.…”

Section: E Parallel and Distributed-basedmentioning

confidence: 99%

Scalable Clustering Algorithms for Big Data: A Review

2021

View full text Add to dashboard Cite

Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.

show abstract

Section: E Parallel and Distributed-basedmentioning

confidence: 99%

Scalable Clustering Algorithms for Big Data: A Review

2021

View full text Add to dashboard Cite

show abstract

“…Most of these algorithms use an elitism-based strategy [163]. The application of evolutionary algorithms in BMI-based systems can be categorized into four major groups of filter design [57,58,[164][165][166], feature selection [61,[167][168][169][170][171][172][173][174], channel selection and optimal electrodes [52, 58-60, 175, 176], parameter selection and optimal classifier structure [9,68,69,[177][178][179][180][181][182][183][184], and finally improving the performance of control tools [185].…”

Section: Evolutionary Algorithms (Eas)mentioning

confidence: 99%

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

View full text Add to dashboard Cite

show abstract

“…Moreover, speedup models have been considered and fitted to explain the experimental speedups obtained for the different procedures and benchmarks, and a comprehensive summary on the statistical significance of the obtained results in performance and speedups is also provided here. Indeed, the most detailed analysis (with respect to that provided in [19,20]) of the statistical significance of the performance and speedup differences among the considered procedures has allowed us to extract clearer conclusions about the behavior of the new procedure here proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], a large-scale feature selection algorithm based on the abilities of the features to explain the data variance is proposed.Nevertheless, these previous papers [15][16][17][18] have neither considered feature selection from a parallel multi-objective approach nor from a cooperative coevolutionary implementation as we propose here. In [19], we provide a first approach to feature selection by using a parallel multiobjective procedure that is extended in [20] with further benchmarks and a wider performance analysis. The parallel multi-objective procedures proposed there are extended here by considering them from a cooperative coevolutionary point of view.…”

mentioning

confidence: 99%

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

Kimovski

Ortega

Ortíz

et al. 2015

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

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).

show abstract

Feature selection in high-dimensional EEG data by parallel multi-objective optimization

Cited by 9 publications

References 18 publications

Scalable Clustering Algorithms for Big Data: A Review

Scalable Clustering Algorithms for Big Data: A Review

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

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

Contact Info

Product

Resources

About