Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

Intan, Putroue Keumala

doi:10.15642/mantik.2019.5.2.90-99

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…For specificity, the best results are obtained using a Linear kernel at 97.50 %. The Linear kernels explain data distribution better than the Polynomial and Gaussian kernels in the data mapping process [44]. Based on these results, shape feature extraction-SVM classification obtains the best results using the Linear kernel with 0.04 seconds for computational time.…”

Section: Classificationmentioning

confidence: 83%

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Mu'jizah

Novitasari

2021

J. Teknol. dan Sist. Komput

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Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

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Section: Classificationmentioning

confidence: 83%

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Mu'jizah

Novitasari

2021

J. Teknol. dan Sist. Komput

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“…The first is to add a soft margin to the optimization problem to anticipate the presence of outliers. This is followed by the use of the kernel function to transform the data into a space with higher dimensions so that it can be classified more easily [28].…”

Section: Methodsmentioning

confidence: 99%

Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models

Hertono,

Wattimena,

Mendrofa

et al. 2024

J. Eng. Technol. Sci.

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Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.

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“…In solving classification problems using non-linearly separable datasets, this algorithm uses a kernel function (kernel trick) for mapping the data into a high-dimensional feature space to obtain a hyperplane that separates the data into two classes [30]. Some of the more popular kernel functions often used in SVM are the polynomial, RBF, and sigmoid functions; these kernel functions use equations ( 4) to (6) to generate a hyperplane in the classification process [31].…”

Section: Support Vector Machinementioning

confidence: 99%

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

Batubara¹,

Zarlis²,

Rosnelly³

2023

ICoSTEC

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In the era of technology 4.0, a system is needed to support the development of a company, both in industry, education, and others to help solve problems. In this study, the authors used the Backpropagation Neural Network Algorithm in recognizing hyperthyroid disease patterns. In this study, in the recognition of hyperthyroid disease patterns. The author uses 11 data variables that will be trained using the back-propagation algorithm where the weighting is done randomly and the second data is trained using the backpropagation algorithm. In this study using Matlab application for processing. From the results of testing data derived from kaggle, namely hyperthyroid disease data above, we can see in the 2-2-1 architecture which shows that the target is reduced by the jst output that the SSE is 0.06571 which indicates that there is an increase in hyperthyroid disease activity in humans. From the data obtained, that the performance of artificial neural network calculations with the Backpropagation Algorithm is 86%. Can be seen by comparing the desired target with the prediction target.

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Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

Cited by 12 publications

References 8 publications

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

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