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Procedure of 10-fold cross validation.
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Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussian radial basis function kernel are choosen to determine pupils who have difficulties in writing. The 10-fold cross-validation method...
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... this project, we used 10-fold cross validation (k = 10) as it is the most common used for data mining and machine learning. As shown in Figure 1, the darker section of the data are used for training while the remaining data; lighter sections are used for validate the model. This process is repeated 10 times until all sections have been validated. ...
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Citations
... SVM additionally proves effective on data with large dimensions [13]. SVM has also been used satisfactorily in a variety of applications, including covid-19 forecasting [14], identifying leaf diseases [15] [16], forecasting stock prices [17], classifying handwriting [18], identifying fraudulent banking [19], identifying fraudulent credit cards [20], recognition of faces [21], and numerous others. Based on the results of the previous research, this research will develop the SVM method to predict health insurance claims. ...
Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.
... SVM also works well in high dimensional data [7]. SVM has been implemented successfully in many applications, such as covid-19 prediction [8], leaf diseases detection [9], [10], stock price prediction [11], handwriting classification [12], bank fraud detection [13], credit card fraud detection [14], face recognition [15], and many other applications. However, the SVM performance is determined by the parameters and the features [16]. ...
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Handwriting analysis is the science of determining an individual’s personality from his or her handwriting by assessing features such as slant, pen pressure, word spacing, and other factors. Handwriting analysis has a wide range of uses and applications, including dating and socialising, roommates and landlords, business and professional, employee hiring, and human resources. This study used the ResNet and GoogleNet CNN architectures as fixed feature extractors from handwriting samples. SVM was used to classify the writer’s gender and age based on the extracted features. We built an Arabic dataset named FSHS to analyse and test the proposed system. In the gender detection system, applying the automatic feature extraction method to the FSHS dataset produced accuracy rates of 84.9% and 82.2% using ResNet and GoogleNet, respectively. While the age detection system using the automatic feature extraction method achieved accuracy rates of 69.7% and 61.1% using ResNet and GoogleNet, respectively
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Gliomas are most common brain tumor in children and adults
worldwide and accounts for 80% of all malignant tumors. In
this work, we proposed a novel method for glioma grade
classification using texture feature set extracted from
T2-weighted magnetic resonance images (MRI). Gray-level
co-occurrence matrix (GLCM) parameters are computed from
local Optimal Oriented Pattern (LOOP) transformed images
to differentiate low grade and high grade glioma.
Classification is carried out using support vector machine
(SVM), Naive Bayes and k-nearest neighbor (k-NN) classifier
and their performance for glioma grade classification is
accessed. SVM classifier outperforms other classifiers and
achieved an accuracy of 95%, sensitivity of 93% and
specificity of 100% for classifying gliomas using proposed
LOOP transformed based GLCM texture features.
... Cross-validation is a popular and widely used model validation technique [19][20][21][22] for assessing how the results of machine learning analysis will generalize to an independent dataset. It is mainly used in settings where the goal was prediction, and one wants to estimate how accurately a predictive model will perform in practice. ...
Upper body power (UBP) is an important determinant of cross-country ski race performance. Although numerous studies exist to measure UBP of cross-country skiers, to date, no study has ever attempted to predict UBP of cross-country skiers. The purpose of this paper was to develop prediction models for estimating 10-s UBP (UBP10) and 60-s UBP (UBP60) of cross-country skiers using support vector machines (SVM). Four types of SVMs have been considered, they are as follows: SVM using the radial basis function kernel (SVM-RBF), SVM using the sigmoid kernel, SVM using the polynomial kernel, and SVM using the linear kernel. For comparison purposes, UBP prediction models based on multilayer perceptron and multiple linear regression have also been developed. The dataset used in this study includes data of 77 subjects. Age, gender, height, weight, body mass index, maximal heart rate, maximal oxygen uptake, and exercise time are the predictor variables, and UBP10 and UBP60 are the target variables. Several UBP prediction models have been developed by using the combination of the predictor variables to predict UBP10 and UBP60. By using 10-fold cross-validation on the datasets, the performance
of the models has been evaluated by calculating their standard error of estimates (SEEs) and multiple correlation coefficients (Rs). The results show that SVM-RBF based UBP prediction models perform much better (i.e., yield lower SEEs and higher Rs) than the prediction models developed by other regression methods and can be safely used for the prediction of UBP of cross-country skiers.
Age detection from handwritten documents is a crucial research area in many disciplines such as forensic analysis and medical diagnosis. Furthermore, this task is challenging due to the high similarity and overlap between individuals’ handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of age-related features suggested by a graphologist, to detect the age of the writers, have been proposed. These features include irregularity in slant, irregularity in pen pressure, irregularity in textlines, and the percentage of black and white pixels. Support Vector Machines (SVM) classifier has been used to train, validate and test the proposed approach on two different datasets: the FSHS and the Khatt dataset. The proposed method has achieved a classification rate of 71% when applied to FSHS dataset. Meanwhile, our method outperformed state-of-arts methods when applied to the Khatt dataset with a classification rate of 65.2%. Currently, these are the best rates in this field.KeywordsAge detectionMachine learningImage processingHandwriting analysis
Gender detection from handwritten documents is a crucial research area in many disciplines such as psychology, pyelography, graphology, and forensic analysis. Furthermore, this task is challenging due to the high similarity and overlap between individuals’ handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of gender-related features suggested by a graphologist, to detect the gender of the writers, have been proposed. These features include margins, space between words, pen-pressure and handwriting irregularity. Both SVM and ANN classifiers have been used to train, validate and test the proposed approach on two different data sets: our data set FSHS and ICDAR2013 dataset. The proposed method has achieved high classification rates of 94.7% and 97.1% using SVM and ANN respectively. Meanwhile, our method outperformed state-of-arts methods when applied to the ICDAR2013 dataset with classification rates of 91.4% and 92.5% using SVM and ANN respectively.
