Fusing sequential minimal optimization and Newton’s method for support vector training

Introduction: The condition of prostate cancer continues to represent a substantial medical issue because it necessitates precise predictive models that support healthcare decisions. A research study investigates how Extreme Gradient Boosting (XGBoost) performs when identifying significant biomarkers to anticipate prostate cancer risks. The research compares XGBoost to Sequential Minimal Optimization (SMO) in Support Vector Machines (SVM) to evaluate its better performance in classification. Prostate-Specific Antigen (PSA) levels together with Gleason scores and molecular markers form the clinical parameters within the dataset. Process data correctly by cleansing it and normalizing it while extracting important features to achieve the optimal model performance. Objectives: This research investigates how well Extreme Gradient Boosting (XGBoost) predicts prostate cancer through an examination of its function to discover complex non-linear data patterns in clinical datasets. The research compares XGBoost to SMO algorithm of Support Vector Machines through a performance assessment of classification metrics including accuracy, precision, recall and F1-score. The research explores two important biomarkers Prostate-Specific Antigen (PSA) levels along with Gleason scores which affect how prostate cancer is expected to progress. The research applies machine learning advanced methods to develop improved early identification systems that lead to better clinical decisions which subsequently enhance patient health results. Methods: The research method relies on a structured machine learning process where multiple steps start with detailed data preprocessing for inconsistent data elimination and variable normalization. Feature extraction technologies increase model interpretability by being applied to the analysis. The available dataset contains two well-separated sections for training and testing purposes which aims to establish reliable validation procedures. Both XGBoost and SVM with SMO operate for classification tasks while their achievement levels are assessed through standard measurement criteria which include accuracy, precision, recall and F1-score. A logistic regression analysis validates whether PSA levels and Gleason scores are significant factors in predictive modeling. Results: XGBoost achieves superior results over SVM with SMO according to every mitigation evaluation indicator. The classification metric for XGBoost displays 0.95 accuracy whereas SVM with SMO achieves 0.87 accuracy. The evaluation of XGBoost produced precision measurements of 0.92 while recall reached 0.93 and F1-score achieved 0.92. These evaluation scores surpassed SVM with SMO which generated precision of 0.85, recall of 0.80 and F1-score of 0.82. XGBoost manages to lower the chances of overfitting while improving biomarker interpretation so it shows clear potential for clinical prostate cancer prediction applications. Conclusions: The conducted research demonstrates XGBoost's effectiveness for prostate cancer classification through better prediction accuracy along with stronger resistance than SVM with SMO. The research demonstrates AI-powered models should become an integral part of clinical practice to improve the detection and treatment preparation for patients.

Sequential Minimal Optimization (SMO) is one of the state-of-the-art solutions for Support Vector Machines (SVM) training. SMO sequentially picks a pair of training samples for joint optimization, which minimizes the usage of memory. However, to find such two samples, SMO uses one outer loop through the entire training set looking for a sample which violates the KKT conditions, and one inner loop to look for a sample which heuristically maximizes the increase of the SVM objective function. While there is limited room for improvement in the inner loop, the outer loop can be reduced significantly if we can locate a small subset of training samples which potentially contains the Support Vectors (SVs).We present a Fisher Discriminant Analysis(FDA) based approach to locate potential SVs in a small core subset, which enables the SVM training to be focused effectively on a promising small region instead of the entire SV-sparse training set. Nevertheless, the training points outside the subset can not be blindly ignored. The method iteratively trains the classifier on the core subset and tune it using the remainder subset. Experimental results show that the approach significantly speeds up SMO training by reducing kernel evaluations, which dominates the training time. It converges much faster to optimal solution with much fewer iterations than standard SMO.

Aircraft engine is an important component of the airplane, which lifespan and reliability have directly influence to flight safety. Vibration analysis is an effective fault diagnosis method for airplane's mechanical construction fault. Several typical vibration faults have drawn from the vibration analysis on the basis of fault mechanism in aircraft engine, and the standard vibration fault-symptom (Fuzzy Membership) relationship table have constructed based on Fuzzy Mathematic theory in this paper. Sample data are simulated on the basis of this table. Sequential Minimal Optimization (SMO) is a fast algorithm for training Support Vector Machines (SVM). The simulated result showed a great improvement on computation efficiency by selecting double threshold parameter for SMO algorithm than single threshold. In order to examine the algorithm's effect applying in the aircraft engine fault diagnosis, Radial Basis Function (RBF) Neural Network, C4.5 Decision Tree, SVM (chunking), Platt's SMO and Improved SMO algorithm are applied to the training data and test data. It is shown that the Improved SMO algorithm displayed superior comprehensive performance. Finally, the vibration fault feature is reduced based on Fuzzy-Rough Sets theory. The research shows that the Improved SMO algorithm is quicker than other algorithm in aircraft engine fault diagnosis with the reduced feature attributes, and its accuracy is high.

FAST ONLINE TRAINING OF L1 SUPPORT VECTOR MACHINES By Gabriella Melki A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2016. Directors: Vojislav Kecman, Alberto Cano Department of Computer Science This thesis proposes a novel experimental environment (non-linear stochastic gradient descent, NL-SGD), as well as a novel online learning algorithm (OL SVM), for solving a classic nonlinear Soft Margin L1 Support Vector Machine (SVM) problem using a Stochastic Gradient Descent (SGD) algorithm. The NL-SGD implementation has a unique method of random sampling and alpha calculations. The developed code produces a competitive accuracy and speed in comparison with the solutions of the Direct L2 SVM obtained by software for Minimal Norm SVM (MN-SVM) and Non-Negative Iterative Single Data Algorithm (NN-ISDA). The latter two algorithms have shown excellent performances on large datasets; which is why we chose to compare NL-SGD and OL SVM to them. All experiments have been done under strict double (nested) cross-validation, and the results are reported in terms of accuracy and CPU times. OL SVM has been implemented within MATLAB and is compared to the classic Sequential Minimal Optimization (SMO) algorithm implemented within MATLAB’s solver, fitcsvm. The experiments with OL SVM have been done using k-fold cross-validation and the results reported in % error and % speedup of CPU Time. viii

The paper presents the good classification accuracy of Support Vector Machine (SVM) with optimized parameters by particle swarm optimizer (PSO) in Chinese text classification. Through the simulation we also see that its training speed is slow when we deal with large amounts of texts in dataset, and it affects classification performance. Platt's sequential minimal optimization (SMO) is one of the fastest algorithms for training SVMs, so we introduce the distributed SMO using multiple core processors to process the training data as a fast classification of large amounts of texts in dataset.

The network routing problem is one of important problems to consider behaviors of multiple flows on a network with traffic. Some network routing problems can be classified into the selfish routing game in which the behaviors of the flows are competitive and the optimal routing problem in which the behaviors of the flows are cooperative. This study focuses on the optimal routing problem. The optimal routing problem is formulated as an optimization problem with a linear equality constraint and lower limit constraints. The learning problem of the support vector machine (SVM) is formulated as an optimization problem with a linear equality constraint and upper and lower limit constraints. The sequential minimal optimization (SMO) is one of the fast solvers for the learning problem. SMO can be applied to solve the optimal routing problem. This study proposes a new fast optimal routing search method using SMO. In the numerical experiments, the effectiveness of the proposed method on the computational cost is verified with the comparison to the search method using the replicator dynamics.

Sequential Minimal Optimization (SMO) is one of simple but fast iterative algorithm for Support Vector Machine (SVM), while there is a large amount of vector multiplication in SMO, which is still expensive and time-consuming. In this paper, we propose our Semi-sparse algorithm to enhance the vector multiplication in the SMO algorithms for large-scale sparse matrices. In the worst scenario, the traditional sparse algorithm on SMO needs O(n1+n2) times of judgments and addressing on two sparse vectors which own m and n elements respectively, while Semi-sparse algorithm can nearly finish this multiplying process within O(n2). Our experimental results on two benchmarks show that the modified SVMTorch based on our Semi-sparse algorithm can perform significantly faster than SVMTorch based on the original sparse algorithm.

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.

Sequential minimal optimization (SMO) and Karush-Kuhn-Tucker condition are often used to solve learning problems in support vector machines. However, during hardware implementation of the SMO algorithm, enhancing chip performance without excessively increasing chip area is often a crucial issue. The solution proposed in this paper is a novel reconfigurable and efficient chip design with SMO-based training accelerator (REC-STA). Two novel methods used in the proposed REC-STA are trimode coarse-grained reconfigurable architecture (TCRA) and triple finite-state-machine with dynamic scheduling The first method modifies the baseline SMO design by developing trimode reconfigurable architectures with parallel and pipeline computing capabilities. The second method provides a schedule for efficient reconfiguration of the TCRA. Use of these methods can remove kernel cache design. For chip manufacturing, the implementation of the REC-STA is synthesized, placed, and routed using the TSMC 0.18-μm technology library. The core size is 2.94 mm × 2.94 mm and the power consumption is 77.3 mW. Compared with the baseline design, the FPGA simulation results show that the proposed architecture requires 50% less memory and 31% fewer gate counts but provides a 16-fold improvement in training performance. The experimental results confirm the efficacy of the proposed architecture and methods.

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).

This work study a poet identification in Arabic poetry using classification methods, with features like poetry features, Sentence length, Characters, word length, first word in the poetry sentence and specific words are used as input data for text classification algorithms: Sequential Minimal Optimization (SMO), Support Vector Machine (SVM), and Linear discriminant analysis (LDA). The data set of experiment divided into two parts: a training dataset with known poets and test dataset with unknown poets. In our experiment, a set of 114 poets from entirely different eras are used. The researcher shows exciting results with a classification accuracy of 98.2456%.

Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To guarantee its satisfying performance, it is important to set appropriate parameters of SVM algorithm. Sequential Minimal Optimization (SMO) is an effective training algorithm belonging to SVM, i.e.LS_SVM. Therefore, on the basis of the SMO algorithm and LS_SVM, which integrates SMO algorithm and LS_SVM, we introduced Particle Swarm Optimization (PSO) algorithm, and utilized an example to certify its validity. PSO is proposed to deal with the large amount of data, and the simulation results showed the effectiveness of this method.

The responsibility weighted Mahalanobis kernel for semi-supervised training of support vector machines for classification

In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag. Whilst in a single instance learning each instance is connected with the label that contains a single feature vector. This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework. In single-instance learning, two datasets are applied (students’ dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning. Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results. A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning. The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75%, whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of 99.33%.

Background The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to enable automatic classification of unstructured information on smoking status from Swedish EMR data. Methods Data on patients’ smoking status from EMRs were used to develop 32 different predictive models that were trained using Weka, changing sentence frequency, classifier type, tokenization, and attribute selection in a database of 85,000 classified sentences. The models were evaluated using F-score and accuracy based on out-of-sample test data including 8500 sentences. The error weight matrix was used to select the best model, assigning a weight to each type of misclassification and applying it to the model confusion matrices. The best performing model was then compared to a rule-based method. Results The best performing model was based on the Support Vector Machine (SVM) Sequential Minimal Optimization (SMO) classifier using a combination of unigrams and bigrams as tokens. Sentence frequency and attributes selection did not improve model performance. SMO achieved 98.14% accuracy and 0.981 F-score versus 79.32% and 0.756 for the rule-based model. Conclusion A model using machine-learning algorithms to automatically classify patients’ smoking status was successfully developed. Such algorithms may enable automatic assessment of smoking status and other unstructured data directly from EMRs without manual classification of complete case notes.