A comprehensive survey on support vector machine classification: Applications, challenges and trends

Cervantes, Jair; García-Lamont, Farid; Rodríguez-Mazahua, Lisbeth; López, Asdrúbal

doi:10.1016/j.neucom.2019.10.118

Cited by 1,356 publications

(586 citation statements)

References 219 publications

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“…New examples are classified based on their largest distance to the hyperplane. A detailed explanation of the SVM are found in [38] and [39], while examples with applications for text classification using SVMs are found in [40] and [41].…”

Section: B: Support Vector Machines (Svms)mentioning

confidence: 99%

Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century

Neocleous

Loizides

2021

IEEE Access

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Section: B: Support Vector Machines (Svms)mentioning

confidence: 99%

Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century

Neocleous

Loizides

2021

IEEE Access

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“…Although the Naive Bayesian classification is an effective model for diagnosis melanoma, the decision tree algorithm is not well suited in this domain [ 57 ]. It is important to consider that SVM is not so popular with large data sets as it requires a significant amount of training time; however, in our research, this was not the case [ 56 ]. In comparison to the latest research work on SVM classification for melanoma (85.19% [ 58 ], 92.1% [ 59 ], 96% [ 60 ], 90% [ 61 ], 97.32% [ 62 ]), we achieved 98.9% accuracy with the proposed technique.…”

Section: Resultsmentioning

confidence: 99%

“…SVM is proven to be the optimal for linearly separable cases and its strategy to determine maximum-margin hyperplane is one of the best to reduce the prediction error [54]. In general, SVM is better for a two-class classification problem with a smaller number of features [55,56]. However, Naive Bayes can handle more features easily.…”

Section: Case 3: Combination Of Spectrophotometry and Hfus Imaging Tementioning

confidence: 99%

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

et al. 2020

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Dermatoscopy, high-frequency ultrasonography (HFUS) and spectrophotometry are promising quantitative imaging techniques for the investigation and diagnostics of cutaneous melanocytic tumors. In this paper, we propose the hybrid technique and automatic prognostic models by combining the quantitative image parameters of ultrasonic B-scan images, dermatoscopic and spectrophotometric images (melanin, blood and collagen) to increase accuracy in the diagnostics of cutaneous melanoma. The extracted sets of various quantitative parameters and features of dermatoscopic, ultrasonic and spectrometric images were used to develop the four different classification models: logistic regression (LR), linear discriminant analysis (LDA), support vector machine (SVM) and Naive Bayes. The results were compared to the combination of only two techniques out of three. The reliable differentiation between melanocytic naevus and melanoma were achieved by the proposed technique. The accuracy of more than 90% was estimated in the case of LR, LDA and SVM by the proposed method.

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“…These data can be plotted on a 2D coordinate system (i.e., PCA score plot). With known classes (e.g., stressed vs. healthy) of the data, it is possible to draw a line that can best separate all of the data into two classes; this line is called a decision boundary (demonstrated in Figure 12 [127]). The procedure can also be used for three or more dimensions of data, where the boundary becomes a plane for three dimensions or a hyperplane for dimensions higher than three.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Zubler

Yoon

2020

Biosensors

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Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

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A comprehensive survey on support vector machine classification: Applications, challenges and trends

Cited by 1,356 publications

References 219 publications

Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century

Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

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