Preictal phase detection on EEG signals using hybridized machine learning classifiers with a novel feature selection procedure based GAs and ICOMP

Kocadağlı, Ozan; Özer, Ezgi; Batista, Arnaldo

doi:10.1016/j.eswa.2022.118825

Cited by 10 publications

(1 citation statement)

References 39 publications

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“…The researchers employed an improved genetic algorithm as a feature selection method to select the best set of features from the NSL-KDD dataset [30]. Researchers have introduced an effective hybrid training scheme to classify EEG signals to accurately detect pre-epileptic seizures [31]. Researchers proposed a binary ocean predator algorithm (BMPA-TVSinV) for feature selection [32].…”

Section: Introductionmentioning

confidence: 99%

An Image Feature Extraction Algorithm Based on Tissue P System

Huang,

Song,

Han

et al. 2024

J. Phys.: Conf. Ser.

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As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence.

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Section: Introductionmentioning

confidence: 99%