A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI
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TL;DR: Experimental results show that the wrapper method presented in this paper is able to obtain very small subsets of features, which are quite stable and also achieve high classification accuracy, regardless of the classifiers used.
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About: This article is published in Neurocomputing. The article was published on 14 Mar 2019. and is currently open access. The article focuses on the topics: Feature selection & Evolutionary algorithm.
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Citations
Review of swarm intelligence-based feature selection methods
TL;DR: A comparative analysis of different feature selection methods is presented, and a general categorization of these methods is performed, which shows the strengths and weaknesses of the different studied swarm intelligence-based feature selection Methods.
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Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
TL;DR: A systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem is presented.
Brain-Computer Interface: Advancement and Challenges.
Muhammad F. Mridha,Sujoy Chandra Das,Muhammad Mohsin Kabir,Aklima Akter Lima,Md. Rashedul Islam,Yutaka Watanobe +5 more
TL;DR: In this paper, a comprehensive overview of the brain-computer interface (BCI) domain is presented, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers.
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A novel community detection based genetic algorithm for feature selection
TL;DR: In this paper, the authors proposed a genetic algorithm based on community detection, which functions in three steps, where feature similarities are calculated in the first step and features are classified by community detection algorithms into clusters throughout the second step In the third step, features are picked by a GA with a new community-based repair operation.
Binary coyote optimization algorithm for feature selection
Rodrigo Clemente Thom de Souza,Rodrigo Clemente Thom de Souza,Camila Andrade de Macedo,Camila Andrade de Macedo,Leandro dos Santos Coelho,Leandro dos Santos Coelho,Juliano Pierezan,Viviana Cocco Mariani +7 more
TL;DR: A binary version of the COA, named Binary COA (BCOA) applying to select the optimal feature subset for classification, based on the hyperbolic transfer function in a wrapper model is proposed.
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References
Enhancing LDA-based discrimination of left and right hand motor imagery: Outperforming the winner of BCI Competition II
Raoof Masoomi,Ali Khadem +1 more
- 01 Nov 2015
TL;DR: A better misclassification rate is attempted while selecting less features compared with various former reported researches on this dataset using linear discriminant analysis (LDA) as the classifier.
Multiresolution analysis over simple graphs for brain computer interfaces
TL;DR: A new graph-based transform for wavelet lifting and a tailored simple graph representation for electroencephalography (EEG) data are proposed, which results in an MRA system where temporal, spectral and spatial characteristics are used to extract motor imagery features from EEG data.