Parameter selection of Gaussian kernel SVM based on local density of training set

Yang, Jiawei; Wu, Zeping; Peng, Kang; Okolo, Patrick N.; Zhang, Weihua; Zhao, Hailong; Sun, Jianfei

doi:10.1080/17415977.2020.1797716

Cited by 21 publications

(4 citation statements)

References 40 publications

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“…Different SVM algorithms use different types of kernel functions. In the current study, Gaussian radial basis function (RBF) SVM (GSVM) is used due its excellent learning performance [ 41 ] in many applications including EEG-based emotion recognition [ 12 , 42 , 43 ]. CART classifiers use a minimum cost-complexity pruning technique [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Yuvaraj

Thangavel

Thomas

et al. 2023

Sensors

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Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.

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Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Yuvaraj

Thangavel

Thomas

et al. 2023

Sensors

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“…SVM is a binary classification model that maps training data into a feature space by means of a kernel function and finds the best hyperplane that separates data points of one class from data points of another class. Different kernel functions can lead to completely different properties (Yang et al, 2021). Popular kernel functions include linear, non-linear, radial basis function (RBF), sigmoid and Gaussian kernel functions, which are suitable for different applications (Agarwal and Kumar, 2016).…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning

et al. 2023

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Most satellite-derived bathymetry (SDB) methods developed thus far from passive remote sensing data have required in situ water depth, thus limiting their utility in areas with no in situ data. Recently, new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) observations have shown great potential in providing high-precision a priori water depth benefits from range-resolved lidar. In this study, we propose a combined active and passive remote sensing SDB method using only satellite data. An adaptive ellipse DBSCAN (AE-DBSCAN) algorithm is introduced to derive a priori bathymetric data from ICESat-2 data to automatically adapt to the terrain change complexity, and then we use these a priori bathymetric data in Sentinel-2 images to help build a model between remote sensing reflectance (Rrs) and water depth. Three machine learning (ML) methods are then employed, and the performances compared with conventional empirical SDB models are presented. After that, the results using different Sentinel-2 Rrs band combinations and the effects with and without atmospheric correction on ML-based SDB are discussed. The results showed that our AE-DBSCAN method performs better than the standard DBSCAN method, and the ML-based SDB can achieve an overall RMSE of less than 1.5 m in St. Thomas better than the traditional SDB method. They also indicate that ML-based SDB can obtain a relatively high-precision water depth without atmospheric correction, which helps to improve processing efficiency by avoiding a complex atmospheric correction process.

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“…It is an effective kernel method when there is a nonlinear relationship between class labels and attributes ( Apostolidis-Afentoulis, 2015 ). In addition, the RBF kernel also provides simplified tuning by using only two parameters: gamma (γ), which adjusts the smoothness of the hyperplane by changing its flexibility ( Shadeed et al, 2020 ), and the penalty parameter (C), which adjusts the tolerance to data points shifted from their sides ( Yang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

ÖZER,

ÖZBEK SARICA,

ARĞA

2023

Turkish Journal of Biology

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Background/aim The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease. Materials and methods In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk’s lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets. Results It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis. Conclusion By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor ( https://doi.org/10.18129/B9.bioc.SVMDO ) and GitHub ( https://github.com/robogeno/SVMDO ).

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Parameter selection of Gaussian kernel SVM based on local density of training set

Cited by 21 publications

References 40 publications

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

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