Proximal support vector machine techniques on medical prediction outcome

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.

Support Vector Machines and Kernel methods have become very popular as methods for learning from examples. In this paper, in order to solve the non-linear and the time of recognition problem, recent advanced techniques are presented. For example, Kernel Principal Components and Proximal Support Vector Machines, Kernel Principal Components Analysis can describe multiple correlations between pixels in the image recognition, and can handle the high order statistics of the original data, at the same time the nonlinear features of the image can be preferably extracted. Proximal Support Vector Machines is an improvement for these characters of Support Vector Machines, combines the proximal algorithm with Support Vector Machines, has a better ability of nonlinear mapping and a stronger generalization capability, and the pace of Proximal Support Vector Machines is very enhanced. In this paper, the computer simulation was progressed based on ORL face database, the effectiveness of the Proximal Support Vector Machines algorithm and Kernel Principal Components Analysis algorithm were shown by experimental results.

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.

Proximal support vector machines (PSVMs) are support vector machines (SVMs) version which involve equality instead of inequality constraints assigning data points to one of two classes, and have a significant speedup in comparison with traditional SVM-type classifiers. However, PSVMs are very sensitive to outliers or noises because of overfitting problem. In this paper, a robust proximal support vector machines (RPSVMs) is proposed to deal with the problem of overfitting in PSVMs for two-class classification. In experiments, two methods are used to give membership values to training data points according to their relative importance in the training set. Results show that the proposed RPSVMs actually reduce the effect of outliers and yield higher classification rate than PSVMs do

Soft computing approach to fault diagnosis of centrifugal pump

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

Proximal support vector machine is a variation of standard support vector machine and can be trained extremely efficiently for binary classification. However in many application fields, multi-class classification and incremental learning must be supported. Incremental linear proximal support vector classifier for multi-class classification has been developed in recent years, but only its performance in one-against-all manner has been investigated, and the application of proximal support vector machine for nonlinear multi-class classification has not been studied. In order to apply proximal support vector machine to more fields, three multi-class classification policies (one-against-all, one-against-one, DAGSVM) applied to incremental linear proximal support vector classifier are compared and incremental nonlinear proximal support vector classifier for multi-class classification based on Gaussian kernel is investigated in the paper. The experiments indicate that one-against-all policy is best for incremental linear proximal support vector classifier according to the tradeoff between computing complexity and correctness, and the introduced incremental nonlinear proximal support vector classifier is effective in one-against-all manner when the reduce rate is below 0.6.

The internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continuously, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, intrusion detection technique is a valuable technology to protect target systems and networks against malicious activities. But this system doesn't provide the required accuracy. Thus to meet this requirement, this paper proposes an intrusion detection system as a model based on Proximal Support Vector Machines (PSVMs) implemented with various combination of basic kernel functions. PSVM is a light and simple modification of support vector machine. We have implemented PSVM for binary classification of intrusion detection data. For experimental training and testing NSL- KDD dataset is preprocessed using Principle Component Analysis technique. Using proposed classification model, we have achieved up to 79% classification accuracy.

In this paper, we have proposed a novel model called proximal support matrix machine (PSMM), which is mainly based on the models of proximal support vector machine (PSVM) and low rank support matrix machine (LRSMM). In design, the PSMM model has comprehensively considered both the relationship between samples of the same class and the structure of rows or columns of matrix data. To a certain extent, our novel model can be regarded as a synthesis of the PSVM model and the LRSMM model. Since the PSMM model is an unconstrained convex problem in essence, we have established an alternating direction method of multipliers algorithm to deal with the proposed model. Finally, since a great deal of experiments on the minst digital database show that the PSMM classifier has a good ability to distinguish two digits with little difference, it encourages us to conduct more complex experiments on MIT face database, INRIA person database, the students face database and Japan female facial expression database. Meanwhile, the final experimental results show that PSMM performs better than PSVM, twin support vector machine, LRSMM and linear twin multiple rank support matrix machine in the demanding image classification tasks.

This study explores various Support Vector Machine (SVM) variants and provides an analytical comparison to identify their effectiveness in detecting Goods and Services Tax (GST) fraud. GST fraud poses significant challenges to regulatory authorities and businesses, necessitating robust detection methods. SVM, a powerful machine learning algorithm, offers promise in this domain due to its ability to handle complex data and nonlinear relationships. Through a comprehensive examination of SVM variants, including linear SVM, polynomial SVM, and radial basis function SVM, this study assesses their performance in GST fraud detection. Additionally, computational efficiency and scalability are investigated to gauge the practical viability of each variant. The findings contribute to advancing the understanding of SVM's applicability in fraud detection contexts and offer insights into selecting the most suitable variant for GST fraud identification. Ultimately, this research aids stakeholders, including tax authorities and businesses, in implementing effective strategies to combat fraudulent activities and uphold fiscal integrity.

Class imbalance and noisy data widely exist in real-world problems, and the support vector machine (SVM) is hard to construct good classifiers on these data. Fuzzy SVMs (FSVMs), as variants of SVM, use a fuzzy membership function both to reflect the samples’ importance and to remove the impact of noises, and employ cost-sensitive technology to address the class imbalance. They can handle the noise and class imbalance problems in many cases; however, the fuzzy membership functions are often affected by the class imbalance data, leading to inaccurate measures for samples’ performance and affecting the performance of FSVMs. To solve this problem, we design a new fuzzy membership function and combine it with cost-sensitive learning to deal with the class imbalance problem with noisy data, named Slack-Factor-based FSVM (SFFSVM). In SFFSVM, the relative distances between samples and an estimated hyperplane, called slack factors, are used to define the fuzzy membership function. To eliminate the impact of class imbalance on the function and gain more accurate samples’ importance, we rectify the importance according to the positional relationship between the estimated hyperplane and the optimal hyperplane of the problem, and the slack factors of samples. Comprehensive experiments on artificial and real-world datasets demonstrate that SFFSVM outperforms other comparative methods on F1, MCC, and AUC-PR metrics.

This research has three parts addressing classification, especially the imbalanced data problem, which is one of the most popular and essential issues in the domain of classification. The first part is to study the Adaptive Boosting (AdaBoost) algorithm. AdaBoost is an effective solution for classification, but it still needs improvement in the imbalanced data problem. This part proposes a method to improve the AdaBoost algorithm using the new weighted vote parameters for the weak classifiers. Our proposed weighted vote parameters are determined not only by the global error rate but also by the classification accuracy rate of the positive class, which is our primary interest. The imbalanced index of the data is also a factor in constructing our algorithms. The numeric studies show that our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of the F--1 Measure. Theoretic proofs of the advantages of our proposed algorithms are presented. The second part treats the Relevance Vector Machine (RVM), which is a supervised learning algorithm extended from the Support Vector Machine (SVM) based on the Bayesian sparsity model. Compared with the regression problem, RVM classification is challenging to conduct because there is no closed-form solution for the weight parameter posterior. The original RVM classification algorithm uses Newton's method in optimization to obtain the mode of weight parameter posterior, then approximates it by a Gaussian distribution in Laplace's method. This original model would work, but it just applies the frequency methods in a Bayesian framework. This part first proposes a Generic Bayesian RVM classification, which is a pure Bayesian model. We conjecture that our algorithm achieves convergent estimates of the quantities of interest compared with the nonconvergent estimates of the original RVM classification algorithm. Furthermore, a fully Bayesian approach with the hierarchical hyperprior structure for RVM classification is proposed, which improves the classification performance, especially in the imbalanced data problem. The third part is an extended work of the second one. The original RVM classification model uses the logistic link function to build the likelihood, which makes the model hard to conduct since the posterior of the weight parameter has no closed-form solution. This part proposes the probit link function approach instead of the logistic one for the likelihood function in RVM classification, namely PRVM (RVM with the Probit link function). We show that the posterior of the weight parameter in our model follows the multivariate normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithm to simplify the Bayesian computation greatly, and its conditional posterior follows a truncated normal distribution. Compared with the original RVM classification model, our proposed one is another pure Bayesian approach and it has a more efficient computation process. For the prior structure, we first consider the Normal-Gamma independent prior to propose a Generic Bayesian PRVM algorithm. Furthermore, the Fully Bayesian PRVM algorithm with a hierarchical hyperprior structure is proposed, which improves the classification performance, especially in the imbalanced data problem.

Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)

Based on pattern spectrum entropy and proximal support vector machine (PSVM), a motor rolling bearing fault diagnosis method is proposed in this paper. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are processed by the pattern spectrum. Secondly, the morphological pattern spectrum entropy, and pattern spectrum values are utilized to identify the fault features of input parameters of PSVM classifiers. The experiment results demonstrate that the pattern spectrum quantifies various aspects of the shape-size content of a signal, and PSVM costs a little time and has better efficiency than the standard SVM.

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs; in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm.