Computational performance optimization of support vector machine based on support vectors

Wang, Xuesong; Huang, Fei; Cheng, Yuhu

doi:10.1016/j.neucom.2016.04.059

Cited by 18 publications

(6 citation statements)

References 26 publications

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“…In addition to accuracy, area under the ROC curve (i.e., AUC) was also used to measure the performance of the custom shallow CNN with SoftMax and the custom VGG16 with SoftMax. This was done because it considers the entire range of threshold values between 0 and 1 and is not affected by class distribution and misclassification cost 11 , 48 , 49 . The AUC can be treated as a measure of separability, and the lines belonging to a class that reaches close to the top-left corner are the most separable one.…”

Section: Methodsmentioning

confidence: 99%

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Yadav,

Burks,

Qin

et al. 2024

J. Appl. Rem. Sens.

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Section: Methodsmentioning

confidence: 99%

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Yadav,

Burks,

Qin

et al. 2024

J. Appl. Rem. Sens.

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“… SVM is a supervised learning algorithm developed in 1992 by Boser, Guyon, and Vapnik based on statistical learning theory. 23 The SVM algorithm has shown strong performance in solving classification and regression problems with small sample sizes, nonlinearity, and high dimensionality. The 2D scatterplot and 3D scatterplot show that the feature subset has some linear divisibility in spatial distribution, so we try to use the SVM algorithm for breast cancer classification and diagnosis task.…”

Section: Methodsmentioning

confidence: 99%

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

Zhou,

Hu,

Wei

et al. 2024

Technol Cancer Res Treat

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Introduction The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. Objectives This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. Methods Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. Results The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. Conclusion The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

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“…Due to limited sample information, we sought the best compromise between complexity of the model and learning ability in order to obtain the best generalization ability. By determining the hyperplane in the input space, data samples are divided into multiple classes to maximize separation between classes (Wang et al, 2016). When SVM is applied to regression fitting analysis, we can identify the optimal classification surface to minimize errors for all training samples from the optimal classification surface.…”

Section: Svm Analysismentioning

confidence: 99%

Nondestructive determination of carotenoids in kiwifruit leaves infected with Pseudomonas syringae pv. actinidiae by surface‐enhanced Raman spectroscopy combined with chemical imaging

et al. 2023

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Surface-enhanced Raman spectroscopy (SERS), a noninvasive and nondestructive technique, was used to distinguish healthy and cankered kiwifruit leaves. Silver nanoparticles with iodide and calcium ions (Ag@ICNPs) were synthesized as SERS substrates and significantly enhanced the characteristic signal peaks of carotenoids.Due to its simple operation and high sensitivity, the detection process is straightforward and efficient. Using a 532 nm laser excitation source, spectra were collected from the leaf surface over a wavelength range of 400-2000 cm −1 , and the characteristic carotenoid peak at 1520 cm −1 was selected for integral imaging. Detection of changes in carotenoid spectral characteristics on the surface of kiwifruit leaves could be used as an early warning of kiwifruit canker. Raman spectroscopy and high-performance liquid chromatography confirmed the existence of two main carotenoids, lutein and β-carotene, and their intensity changed significantly following infection, especially in the later stages of infection. The results of principal component analysis and support vector machine (SVM) analysis of preprocessed spectra showed that samples of healthy, early disease and advanced disease leaves clustered separately, and the accuracy of SVM classification was up to 98%. The results of chemical imaging showed that the average carotenoid signal decreased with extended incubation time and there were significant differences among healthy, early disease and advanced disease stages. Therefore, SERS can be used as a general tool for in situ nondestructive determination of surface carotenoid changes in kiwifruit leaves during disease, and thereby effectively monitor the nutritional quality of kiwifruit before harvest.

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Computational performance optimization of support vector machine based on support vectors

Cited by 18 publications

References 26 publications

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

Nondestructive determination of carotenoids in kiwifruit leaves infected with Pseudomonas syringae pv. actinidiae by surface‐enhanced Raman spectroscopy combined with chemical imaging

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