Support Vector Machine Based Educational Resources Classification

Xia, Tian

doi:10.7763/ijiet.2016.v6.809

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…The SVM creates a hyperplane is utilized for classification and regression [66]. The SVM builds the model by constructing a hyperplane that separates the provided data, by maximizing the distance between the two clusters [67]. It detects the closest data vectors to the decision boundary in the training dataset, known as the support vectors (SVs), then categorizes a specific test vector using only those nearest data vectors [68].…”

Section: Support Vector Machinesmentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

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Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.

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Section: Support Vector Machinesmentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…The objective of the SVM is to find the highest minimum distance between any two points of data quickly Sakthivel et al [16]. SVM locates the closest data vectors called support vectors (SV) to the decision boundary in the training set, so it depends on these vectors to classify a given new test vector Xia et al [17]. SVM can be handled two problems of input space: linear and nonlinear Awad et al [5], the default formulation of SVM is used to classifier linear problem while the nonlinear problem of input space will be separable, therefore the kernel function can be used to transform the data to a higher-dimensional space, which referred to as "kernel space".…”

Section: Support Vector Machinementioning

confidence: 99%

Support Vector Machine (SVM) for Colorization the Grayscale Image

Alrubaie

Hassoon

2020

QJES

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Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images. There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.

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“…It is based on the geometrical interpretation. The algorithm searches for optimal separating surface, such as hyperplane [12,13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Machine Learning Techniques to Earlier Predict Student's Performance

Tanuar

Heryadi

Lukas

et al. 2018

2018 Indonesian Association for Pattern Recognition International Conference (INAPR)

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Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep Learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.

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Support Vector Machine Based Educational Resources Classification

Cited by 9 publications

References 4 publications

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Support Vector Machine (SVM) for Colorization the Grayscale Image

Using Machine Learning Techniques to Earlier Predict Student's Performance

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