Performance Analysis of Linear Kernel Support Vector Machine Models on Real-World Datasets

A Linear Support Vector Machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with a maximum margin. LSVM typically has higher classification accuracy for linearly separable data than nonlinearly separable data. For this type of data, Support Vector Selection and Adaptation (SVSA) method was developed that uses the support vectors obtained by LSVM and adapts them with respect to training dataset. However, the SVSA's classification performance may not be very satisfactory for linearly separable data in comparison to LSVM depending on the dataset used. In this paper, a hybrid model was presented that combines the results of LSVM and the SVSA efficiently. The main idea of the hybrid model is to utilize the performance of LSVM with the SVSA as LSVM model is already available during implementation of the SVSA. The method which uses the proposed hybrid model is called the Hybrid Support Vector Selection and Adaptation method (HSVSA). In order to show the effectiveness of the proposed model, one real multispectal and two hyperspectral dataset were experimented with the SVSA, LSVM, nonlinear SVM and the HSVSA in the classification. The results showed that when LSVM performs better than the SVSA, the HSVSA achieves LSVM's performance with the hybrid model, and vice versa.

Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machine (PSVM) is proposed to carry out the wind speed prediction using the available real time wind farm data. In developed PSVM predictor, it is modeled for both linear PSVM predictor and non-linear PSVM predictor. The difference between the developed linear and non-linear PSVM predictor models lies in their applicability of kernel functions to perform effective wind speed prediction. The prediction application is implemented for the set of wind farm data with a wind mill height of 50 m in a manner to minimize the mean square error. The training process of the neural network algorithmic flow is done with the developed LSVM, L-PSVM (Linear PSVM) and N-PSVM (nonlinear PSVM) for predicting the wind speed in renewable energy systems. Results computed are compared with the other types of predictors to prove the effectiveness of the proposed variants of SVM predictors. The simulated results presents the effectiveness of the proposed predictors for the real time wind farm data and the applicability of the predictors for the considered datasets.

The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction accuracy of 95% (BRAF-V600E) and 100% (HIV Integrase) and 3D-QSAR prediction accuracy of 76% (BRAFV600E) and 82% (HIV Integrase) was observed. To help explain the better performance of SVM in the comparison reported here and to help assess the degree to which a SVM or 3D-QSAR model is likely to perform best for other targetligands of interest a new EPP (Expected Predictive Performance) metric is introduced. How EPP can be used to help predict future performance of SVM and 3D-QSAR models by quantifying the degree of similarity between candidate compounds and training data is also demonstrated. Results show that the EPP metric is capable of predicting future prediction accuracy of SVM and 3D-QSAr models within 7% of actual performance.

Construction accident narrative classification: An evaluation of text mining techniques

A linear support vector machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with the maximum margin. The LSVM typically has high classification accuracy for linearly separable data. However, for nonlinearly separable data, it usually has poor performance. For this type of data, the Support Vector Selection and Adaptation (SVSA) method was developed, but its classification accuracy is not very high for linearly separable data in comparison to LSVM. In this paper, we present a new classifier that combines the LSVM with the SVSA, to be called the Hybrid SVM and SVSA method (HSVSA), for classification of both linearly and nonlinearly separable data and remote sensing images as well. The experimental results show that the HSVSA has higher classification accuracy than the traditional LSVM, the nonlinear SVM (NSVM) with the radial basis kernel, and the previous SVSA.

Dementia is among the most distressing and burdensome health challenges in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have limitations (e.g., they are based on clinical parameters or are not generalizable). Thus, in this study, prediction models were developed for current and future dementia solely based on demographic, socioeconomic, and health-related features. Demographic, socioeconomic, and health-related variables collected from the Korean Longitudinal Study of Ageing (KLoSA) were used to develop machine learning-based prediction models for current and future dementia with various algorithms. Two sampling strategies were used for feature selection, one based on domain knowledge and the other based on statistical testing. Hyperparameter tuning was performed using grid search with cross-validation on the training set, and model evaluation was conducted on a separate test set. In the initial no-follow-up dataset, 92 of 6,898 participants exhibited dementia. Among 6,207 participants without dementia initially, 69 developed dementia within 2 years. Linear support vector machine (SVM) and radial bias function SVM exhibited the best sensitivity for current and future dementia (79.4% and 77.7%, respectively). The SHAP (SHapley Additive exPlanations) approach improved the transparency of the model by highlighting the top ten features most strongly associated with increased dementia risk. We achieved reasonably accurate prediction results for dementia using only non-clinical features.

There is a growing body of knowledge indicating long-lasting residual electroencephalography (EEG) abnormalities in concussed athletes that may persist up to 10-year postinjury. Most often, these abnormalities are initially overlooked using traditional concussion assessment tools. Accordingly, premature return to sport participation may lead to recurrent episodes of concussion, increasing the risk of recurrent concussions with more severe consequences. Sixty-one athletes at high risk for concussion (i.e., collegiate rugby and football players) were recruited and underwent EEG baseline assessment. Thirty of these athletes suffered from concussion and were retested at day 30 postinjury. A number of task-related EEG recordings were conducted. A novel classification algorithm, the support vector machine (SVM), was applied as a classifier to identify residual functional abnormalities in athletes suffering from concussion using a multichannel EEG data set. The total accuracy of the classifier using the 10 features was 77.1%. The classifier has a high sensitivity of 96.7% (linear SVM), 80.0% (nonlinear SVM), and a relatively lower but acceptable selectivity of 69.1% (linear SVM) and 75.0% (nonlinear SVM). The major findings of this report are as follows: 1) discriminative features were observed at theta, alpha, and beta frequency bands, 2) the minimal redundancy relevance method was identified as being superior to the univariate t -test method in selecting features for the model calculation, 3) the EEG features selected for the classification model are linked to temporal and occipital areas, and 4) postural parameters influence EEG data set and can be used as discriminative features for the classification model. Overall, this report provides sufficient evidence that 10 EEG features selected for final analysis and SVM may be potentially used in clinical practice for automatic classification of athletes with residual brain functional abnormalities following a concussion episode.

Support Vector Machines (SVMs) is a popular and highly performing classification model with a very good generalization ability for many classification applications. This method uses kernels to classify data that are not linearly separable. The added complexity of the kernels that map data to a higher dimensional space degrade the SVM classifier performance when dealing with large datasets. Moreover, classifying a dataset by choosing an appropriate kernel and finding the best set of its parameters is challenging. Failing in this process can easily cause to an overfit problem.;In this thesis we propose the Piece-wise Linear SVM (PWLSVM) using MagKmeans clustering to address the complexity and computational cost of SVMs. We use a linear SVM to overcome the complexity of dealing with the kernels, and a MagKmeans clustering to cluster the data into balanced groups. MagKmeans which is a supervised technique clusters equal number of each class in one group. It ensures that a linear SVM has balanced training samples for each class and can attain an accurate model.;The detailed mathematical formulation and modeling of the proposed Distributed MagKmeans (D-MagKmeans) is presented. The algorithm uses a Distributed MagKmeans clustering approach to transform the PWLSVM to Distributed Piece-wise Linear SVM (D-PWLSVM). The proposed D-MagKmeans clustering approach makes the MagKmeans clustering work in distributed network by only passing the centroid of each cluster in one node only to its one-hop neighbors. This feature of the D-Magkmeans makes our approach appropriate for a distributed processing and decision making while maximizing privacy and minimizing the communication overload.;The proposed algorithm was validated using four datasets in terms of dimensions on the features and the number of samples. Pima Indian Diabetes, with 768 samples and 8 features, is the smallest dataset of the four. We also examined Abalone, with 4177 samples and 8 features, Waveform, with 5000 samples and 22 features, and EHarmony, with over half million samples and 116 features. The results reveal that a reasonable trade-off is required when dealing with a large dataset. The results also illustrate that PWLSVM and D-PWLSVM outperform SVMs on a relatively large dataset, such as EHarmony, Abalone, and Waveform.

Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.

Abstract: A significant challenge in women’s health is posed by Polycystic Ovary Syndrome (PCOS) because it is a difficult disorder to comprehend and exhibits various symptoms. Thisresearch project suggests the creation of a computerized prediction scheme for PCOS using machine learning algorithms and bioinformatics tools. The aim of this system is to give personalized risk assessment to women thus enabling early detection, and proactive management of PCOS. It combines other intelligible probabilities about PCOS with the use of user provided health data with Machine Learning models such as Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, Radial SVM, Linear SVM and KNeighbours Classifier. Additionally, it provides intuitive predictions regarding PCOS likelihood through ML models, which are based on Random Forest, Logistic Regression, Support Vector Machine (SVM), Naive Bayes (NB), Radial SVM (RSVM), Linear SVM (LSVM) and KNeighbours Classifier (KNC). By way of thorough assessment as well as comparison within these models the system intends to improve precision together with reliability during prediction of PCOS. The major objective is therefore to enable timely intervention along with individualized healthcare strategies that will promote better health outcomes for all women.

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Text Categorization (TC) remains as a potential application area for linear support vector machines (SVMs). Among the numerous linear SVM formulations, we bring forward linear PSVM together with recently proposed distributional clustering (DC) of words to realize its potential in TC realm. DC has been presented as an efficient alternative to conventionally used feature selection in TC. It has been shown that, DC together with linear SVM drastically brings down the dimensionality of text documents without any compromise in classification performance. In this paper we use linear PSVM and its extension Fuzzy PSVM (FPSVM) together with DC for TC. We present experimental results comparing PSVM/FPSVM with linear SVM light and SVMlin on popular WebKB text corpus. Through numerous experiments on subsets of WebKB, we reveal the merits of PSVM and FPSVM over other linear SVMs.

Introduction Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics. Methods In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM). Results Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively. Discussion and conclusion Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

Software logging is an essential programming practice that saves important runtime information that can be used later by software developers for troubleshooting, debugging and monitoring the software. Even though software logging has numerous benefits this practice is underutilized because of lack of any formal guiding principles to developers for making strategic and efficient logging decisions. Logging should be optimized because too much logging can cause performance overheads; sparse logging can leave out vital information that might give clues to developers about the real issues. In absence of any formal guidelines developers rely solely on their domain knowledge and experience while making logging decisions. In order to lessen this effort of making decisions we have proposed a machine learning based framework, Logger4u for if-block logging prediction. We extract and use 28 distinctive static features from the source code helpful in making well informed logging decisions. We use Support Vector Machine (two variants, 1 linear and 1 RBF kernel based) models, Multilayer Perceptron with back propagation model and Random forest model in our work. Our approach gives encouraging results for if-block logging task. The accuracy achieved by the Linear SVM, MLP, Random Forest and kernel SVM are 73.05%, 74.62%, 79.84% and 81.22% respectively