Selecting training sets for support vector machines: a review

Nalepa, Jakub; Kawulok, Michał

doi:10.1007/s10462-017-9611-1

Cited by 282 publications

(152 citation statements)

References 133 publications

(151 reference statements)

Supporting

Mentioning

132

Contrasting

Unclassified

Order By: Relevance

“…SVMs prove to be successful in nonlinear classification problems by mapping non-separable features into a higher dimensional space, a procedure known as the kernel trick which uses kernel functions such as Radial Basis Function (RBF) or polynomial. 62 In DT approaches, the training set is continuously split according to a chosen feature. A feature tree can be explained by two entities, namely decision nodes and leaves.…”

Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

View full text Add to dashboard Cite

Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.npj Digital Medicine (2020) 3:3 ; https://doi.org/10.1038/s41746-019-0207-9 PHOTOPLETHYSMOGRAPHY PPG signal PPG waveform is generated during a cardiac cycle and typically measured at a peripheral site. Therefore, it is essentially a pulse pressure waveform that originates from the heart contraction and propagates through the vascular tree. As blood flow is controlled

show abstract

Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Supervised learning infers classification functions from training labeled data. Major supervised learning approaches are multi-layer perceptron neural networks, decision tree learning, support vector machines and symbolic machine learning algorithms [3]. Unsupervised learning infers functions from the hidden structure of unlabeled data.…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised learning infers functions from the hidden structure of unlabeled data. The major unsupervised learning approaches are clustering (k-means, mixture models and hierarchical clustering), anomaly detection and neural networking (Hebbian learning and generative adversarial networks) [3]. Semi-supervised learning infers classification functions from a large amount of unlabeled data, together with a small amount of labeled data [4].…”

Section: Introductionmentioning

confidence: 99%

“…It thus falls between supervised learning and unsupervised learning. The main semi-supervised learning methods are generative models, low-density separation, graph-based methods and heuristic approaches [3].…”

Section: Introductionmentioning

confidence: 99%

“…In 1963, Vapnik initially proposed a linear classifier, using a maximum-margin hyperplane algorithm [3]. In 1964, Aizerman et al, for the first time, proposed the kernel method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups

et al. 2020

View full text Add to dashboard Cite

Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise, the data are normally first transformed from input space to feature space, and then they are classified. However, carrying out this transformation is not always practical, and the process itself increases the cost of training and prediction. To address these problems, this paper puts forward an SVM kernel, called polygonal fuzzy weighted or PFW, which effectively classifies data without space transformation, even if the groups in question are not linearly separable and have overlapping areas. This kernel is based on Gaussian data distribution, standard deviation, the three-sigma rule and a polygonal fuzzy membership function. A comparison of our PFW, radial basis function (RBF) and conventional linear kernels in identical experimental conditions shows that PFW produces a minimum of 26% higher classification accuracy compared with the linear kernel, and it outperforms the RBF kernel in two-thirds of class labels, by a minimum of 3%. Moreover, Since PFW runs within the original feature space, it involves no additional computational cost.

show abstract

Instantaneous Viral Detection of SARS‐CoV‐2 and Beyond using Electromagnetic Sensing

Al Sayed Ali,

Shafi,

Asadallah

et al. 2023

Advanced Sensor Research

View full text Add to dashboard Cite

This study proposes a highly sensitive portable device that utilizes electromagnetic waves and data analytics for instantaneous Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS‐CoV‐2) detection. The device consists of a Radio Frequency (RF) circuit that interprets reflected and transmitted electromagnetic waves to identify virus signatures in physiologically significant matrices, including human saliva and diluted nasopharyngeal swabs. The sensor's accuracy is validated in both pre‐clinical and clinical settings, where clinical measurements demonstrate an instantaneous detection accuracy of 94%, sensitivity of 95%, and specificity of 97.5% between the sensor's physical parameters and SARS‐CoV‐2 detection. The sensor's accurate real‐time response is due to its unique design and precise modeling techniques. In addition, the same sensing system is tested across different viruses and its ability to differentiate between influenza A, respiratory syncytial, and SARS‐CoV‐2 viruses is proven. Hence this work presents a holistic system that can predict the viral concentration of SARS‐CoV‐2, as well as differentiate between different viruses instantaneously and without adding any amplifying agent.

show abstract

Selecting training sets for support vector machines: a review

Cited by 282 publications

References 133 publications

Photoplethysmography based atrial fibrillation detection: a review

Photoplethysmography based atrial fibrillation detection: a review

PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups

Instantaneous Viral Detection of SARS‐CoV‐2 and Beyond using Electromagnetic Sensing

Contact Info

Product

Resources

About