Lagrangian Support Vector Machine Research Articles

Enhancing Command Recognition in Air Traffic Control Through Advanced Classification Techniques

ABSTRACT This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy.

Fused robust geometric nonparallel hyperplane support vector machine for pattern classification

Recently, introducing nonconvex loss functions in support vector machine (SVM) to improve the robustness against varies noises has been drawing much attention. In this paper, we first construct a new robust capped asymmetric elastic net (CaEN) loss function. Second, we describe a novel robust Huberized kernel-based (HK) loss function and theoretically demonstrate several important properties, such as smoothness, boundness and the trade-off between the standard least squares and the truncated least squares. Finally, we apply the CaEN loss and the HK loss into elastic net nonparallel hyperplane SVM (ENNHSVM) to develop a fused robust geometric nonparallel SVM (FRGNHSVM). The proposed FRGNHSVM not only inherits the advantages of ENNHSVM but also improves the robustness of classification problems. An efficient Pegasos-based DC (difference of convex functions) algorithm is implemented to solve the FRGNHSVM optimization problem. In comparison with four famous SVMs, including Lagrangian SVM, twin SVM, pinball SVM and C-loss twin SVM, experimental results on simulations and twelve UCI datasets show that the proposed FRGNHSVM can often improve more than 5% average prediction accuracy. Moreover, the performance of the high prediction accuracy of FRGHNSVM is more significant along with the ratio of label noise increasing, indicating its superiority in dealing with label-contaminated datasets.

Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization

Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc), sensitivity (Sn), and specificity (Sp), which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively.

Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis

In an environment full of disordered information, the media spreads fake or harmful information into the public arena with a speed which is faster than ever before. A news report should ideally be neutral and factual. Excessive personal emotions or viewpoints should not be included. News articles ought not to be intentionally or maliciously written or create a media framing. A harmful news is defined as those explicit or implicit harmful speech in news text that harms people or affects readers’ perception. However, in the current situation, it is difficult to effectively identify and predict fake or harmful news in advance, especially harmful news. Therefore, in this study, we propose a Bidirectional Encoder Representation from Transformers (BERT) based model which applies ensemble learning methods with a text sentiment analysis to identify harmful news, aiming to provide readers with a way to identify harmful news content so as to help them to judge whether the information provided is in a more neutral manner. The working model of the proposed system has two phases. The first phase is collecting harmful news and establishing a development model for analyzing the correlation between text sentiment and harmful news. The second phase is identifying harmful news by analyzing text sentiment with an ensemble learning technique and the BERT model. The purpose is to determine whether the news has harmful intentions. Our experimental results show that the F1-score of the proposed model reaches 66.3%, an increase of 7.8% compared with that of the previous term frequency-inverse document frequency approach which adopts a Lagrangian Support Vector Machine (LSVM) model without using a text sentiment. Moreover, the proposed method achieves a better performance in recognizing various cases of information disorder.

Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation

Purpose: Neuroimaging has demonstrated altered static functional connectivity in patients with premature ejaculation (PE), while studies examining dynamic changes in spontaneous brain activity in PE patients are still lacking. We aimed to explore the reconfiguration of dynamic functional connectivity (DFC) states in lifelong PE (LPE) patients and to distinguish LPE patients from normal controls (NCs) using a machine learning method based on DFC state features.Methods: Thirty-six LPE patients and 23 NCs were recruited. Resting-state functional magnetic resonance imaging (fMRI) data, the clinical rating scores on the Chinese Index of PE (CIPE), and intravaginal ejaculatory latency time (IELT) were collected from each participant. DFC was calculated by the sliding window approach. Finally, the Lagrangian support vector machine (LSVM) classifier was applied to distinguish LPE patients from NCs using the DFC parameters. Two DFC state metrics (reoccurrence times and transition frequencies) were introduced and we assessed the correlations between DFC state metrics and clinical variables, and the accuracy, sensitivity, and specificity of the LSVM classifier.Results: By k-means clustering, four distinct DFC states were identified. The LPE patients showed an increase in the reoccurrence times for state 3 (p < 0.05, Bonferroni corrected) but a decrease for state 1 (p < 0.05, Bonferroni corrected) compared to the NCs. Moreover, the LPE patients had significantly less frequent transitions between state 1 and state 4 (p < 0.05, uncorrected) while more frequent transitions between state 3 and state 4 (p < 0.05, uncorrected) than the NCs. The reoccurrence times and transition frequencies showed significant associations with the CIPE scores and IELTs. The accuracy, sensitivity, and specificity of the LSVM classifier were 90.35, 87.59, and 85.59%, respectively.Conclusion: LPE patients were more inclined to be in DFC states reinforced intra-network and inter-network connection. These features correlated with clinical syndromes and can classify the LPE patients from NCs. Our results of reconfiguration of DFC states may provide novel insights for the understanding of central etiology underlying LPE, indicate neuroimaging biomarkers for the evaluation of clinical severity of LPE.

Sales Forecasting of Perishable Foods With Multiple Stores and Communities-An Empirical Study of Convenience Stores in Taiwan

Purpose: In recent years, smart retailing has been gradually acknowledged and has slowly impacted countless industries. This study takes smart retailing as the starting point and explores the increased attention on the perishable food (fresh food) market in convenience stores. Remarkable improvements in the general food consumer population in Taiwan and the advent of an aging society with changes in social structure and consumer style are also taken into consideration. As perishable goods (fresh food products) easily spoil, the scrap rate of fresh food in convenience stores has always been a topic of great importance. Design/methodology/approach: Perishable food from well-known convenience store chains in Taiwan is taken as the subject of this study, and sales data of stores in different regions are used to establish sales forecast models for the convenience store chains. The optimal sales forecast model for each store's products was established through a data analysis, and the characteristics and differences of all products are explored to establish decision-making advices for enterprises in ordering perishable food. Multiple stores and perishable foods with two-year sales data of 11 perishable foods in every six stores in different county are adopted to establish the predictive models in this study, including time series, support vector machine (SVM), Lagrangian support vector machine (LSVM), random forest, neural network, generalized linear, and generalized linear mixed models. Findings: Results show that the time series model and SVM have lower prediction error values and better prediction results among all the established sales forecast models. Given the influence of region and population characteristics, varying models are applicable to stores in different regions. Thus, the difficulty of prediction will also vary. Practical implications: Different commodities will have varying levels of prediction difficulty due to dissimilarities in commodity attributes. Convenience stores are generally willing to predict the sales of fresh food through an artificial intelligence model. Different forecasting models should be selected by stores. If one or more forecasting models are used for prediction, the model with a stable forecasting error should be selected for implementation. Originality/value: This research analyzes the products sold in multiple stores and applies different time series and machine learning algorithms to build predictive models. The results show that the most suitable algorithms for stores of products are different. If a convenience store wants to build a predictive model, differentiated models must be established for different stores and commodities.

Generalization performance of Lagrangian support vector machine based on Markov sampling

In this paper, we first establish the generalization bounds of Lagrangian Support Vector Machines (LSVM) based on uniformly ergodic Markov chain (u.e.M.c.) samples. As an application, we also obtain the generalization bounds of LSVM based on strongly mixing sequence, independent and identically distributed (i.i.d.) samples, respectively. The fast learning rates of LSVM for u.e.M.c., strongly mixing sequence and i.i.d. samples are established. We also propose a new LSVM algorithm based on Markov sampling (LSVM̲MS) and show the learning performance of LSVM̲MS for UCI datasets. The experimental results show that the LSVM̲MS can improve obviously the learning performance of the classical LSVM algorithm. If the sampling and training total time is a main concern, the LSVM̲MS algorithm is the preferred method compared the known SVM algorithm based on Markov sampling.

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine.

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis

This study investigated the application of a novel hybrid artificial intelligence (AI)-based classifier for aiding prediction of the prognosis in patients with chronic hepatitis. Nineteen biomarkers on 155 patients with hepatitis from the University California Irvine Machine Learning repository were used as input data. Weights derived by applying the geometric margin maximisation criterion of a Lagrangian support vector machine (LSVM) were used for selecting the features associated with the highest relative importance towards the required classification, i.e. to predict whether a patient with hepatitis would have survived or died. Thus, the 19 initial features were reduced to the 16 most important prognostic factors and were fed into various AI-based classifiers. Results indicated an overall classification accuracy and area under the receiver operating characteristic curve of 100% for the proposed hybrid algorithm, the LSVM multilayer perceptron (MLP), thus demonstrating its potential for aiding prediction of prognosis in patients with hepatitis in a clinical setting.

Feature-driven machine learning to improve early diagnosis of Parkinson's disease

Although advances in speech processing have facilitated the prognostic assessment of patients with Parkinson's Disease (PD), there is no objective method towards its early detection in a clinical setting. This study investigated the application of a novel hybrid Artificial Intelligence-based classifier for aiding early diagnosis of PD. Data on dysphonic measures and clinical scores on 68 subjects were obtained from the University of California-Irvine (UCI) Machine Learning database. Weights derived from a Multi-Layer Perceptron (MLP) were applied for feature selection and their moduli used to rank the input features based on their relative importance towards discriminating between physiological and pathological data patterns. Thus, the initial 27 features were reduced to 20 selected diagnostic factors. This reduced feature set was then input to a Lagrangian Support Vector Machine (LSVM) for classification. The overall performance of this hybrid feature-driven algorithm (MLP-LSVM) was thus compared against commercially available software and classifiers from similar studies. Results indicate an overall classification accuracy and the area under the receiver operating characteristic curve of 100% for the proposed feature-driven algorithm (MLP-LSVM), with relatively faster convergence, thus demonstrating its potential for aiding early diagnosis of PD in a clinical setting.

Survey on SVM and their application in image classification

Life of any living being is impossible if it does not have the ability to differentiate between various things, objects, smell, taste, colors, etc. Human being is a good ability to classify the object easily such as different human face, images. This is time of the machine so we want that machine can do all the work like as a human, this is part of machine learning. Here this paper discusses the some important technique for the image classification. What are the techniques through which a machine can learn for the image classification task as well as perform the classification task with efficiently. The most known technique to learn a machine is SVM. Support Vector machine (SVM) has evolved as an efficient paradigm for classification. SVM has a strongest mathematical model for classification and regression. This powerful mathematical foundation gives a new direction for further research in the vast field of classification and regression. Over the past few decades, various improvements to SVM has appeared, such as twin SVM, Lagrangian SVM, Quantum Support vector machine, least square support vector machine, etc., which will be further discussed in the paper, led to the creation of a new approach for better classification accuracy. For improving the accuracy as well as performance of SVM, we must aware of how a kernel function should be selected and what are the different approaches for parameter selection. This paper reviews the different computational model of SVM and key process for the SVM system development. Furthermore provides survey on their applications for image classification.

Online Robust Lagrangian Support Vector Machine against Adversarial Attack

In adversarial environment such as intrusion detection and spam filtering, the adversary-intruder or spam advertiser may attempt to produce contaminate training instance and manipulate the learning of classifier. In order to keep good classification performance, many robuster learning methods have been proposed to deal with the adversarial attack. Support Vector Machines(SVMs) is a kind of successful approach in the adversarial classification tasks and the investigation of robust SVMs is very popular. However, in many real application, the data including stain instance is coming dynamically. Batch learning which needs retraining when encountering new samples, will consume more computing resources. In this paper, we propose a robust Lagrangian support vector machine (RLSVM) with modified kernel matrix and explore the online learning algorithm on it. The experimental results show the robustness of RLSVM against label noise produced by adversaries under the online adversarial environment.

Real-time acquisition of machining task progress based on the power feature of workpiece machining

The real-time acquisition of machining task progress is one of the most important tasks of production management and is an essential aspect of manufacturing information. Targeted to the machining mode of mixed-category workpieces in job shops, a method for the acquisition of real-time machining task progress is proposed. The method is based on both the power feature of workpiece machining and the incremental learning Lagrangian support vector machine. First, the framework for this method is presented in a straightforward manner. Second, by analysing the characteristics of power change during the machining process, the power feature vector, which reflects the characteristics of workpiece machining, is designed for Lagrangian support vector machine. Then, based on the principle of incremental learning Lagrangian support vector machine, which can address the classification of mixed-category workpieces and the problem of an insufficient number of training samples for training the initial classifiers during the actual machining process, a detailed application of this method is constructed for workpiece classification and the acquisition of machining task progress. Finally, the effectiveness of this method is empirically tested by application to a case study.

Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment

ABSTRACTCardiotocography (CTG) is a standard procedure for fetal monitoring during pregnancy and labor. A number of automated methods for the classification of CTG recordings are based on supervised learning. Machine learning requires a set of parameters that quantitatively describe the acquired signals accompanied by a reference interpretation. This article presents a method of retrospective fetal-state assessment using the results of the fuzzy analysis of delivery outcome attributes. In real clinical datasets the class of signals related to an abnormal fetal state is usually underrepresented, which adversely affects the efficiency of the automated evaluation. Additionally, a method for reducing the disproportion between the class sizes based on the proposed fuzzy model is described. The fuzzy-inference-based learning of the Lagrangian Support Vector Machine (LSVM) increased the resulting efficiency of the fetal-state assessment.

Multi-label Lagrangian support vector machine with random block coordinate descent method

When all training instances and labels are considered all together in a single optimization problem, multi-label support and core vector machines (i.e., Rank-SVM and Rank-CVM) are formulated as quadratic programming (QP) problems with equality and bounded constraints, whose training procedures have a sub-linear convergence rate. Therefore it is highly desirable to design and implement a novel efficient SVM-type multi-label algorithm. In this paper, through applying pairwise constraints between relevant and irrelevant labels, and defining an approximate ranking loss, we generalize binary Lagrangian support vector machine (LSVM) to construct its multi-label form (Rank-LSVM), resulting into a strictly convex QP problem with non-negative constraints only. Particularly, each training instance is associated with a block of variables and all variables are divided naturally into manageable blocks. Consequently we build an efficient training procedure for Rank-LSVM using random block coordinate descent method with a linear convergence rate. Moreover a heuristic strategy is applied to reduce the number of support vectors. Experimental results on twelve data sets demonstrate that our method works better according to five performance measures, and averagely runs 15 and 107 times faster and has 9 and 15% fewer support vectors, compared with Rank-CVM and Rank-SVM.

A regularization on Lagrangian twin support vector regression

Twin support vector regression (TSVR), Lagrangian TSVR (LTSVR) and $$\epsilon$$ -TSVR obtain good generalization and faster computational speed by solving a pair of smaller sized quadratic programming problems (QPPs) than a single large QPP in support vector regression (SVR). In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the $$\epsilon$$ -TSVR is proposed. The contributions of our formulation are as follows: (1) we consider the square of the 2-norm of the vector of slack variables instead of the usual 1-norm to make the objective functions strongly convex. (2) We are solving regression problem with just two systems of linear equations as opposed to solving two QPPs in $$\epsilon$$ -TSVR and TSVR or one large QPP in SVR, which leads to extremely simple and fast algorithm. (3) One significant advantage of our proposed method is the implementation of structural risk minimization principle. However, only empirical risk is considered in the primal problems of TSVR and LTSVR due to its complex structure and thus may incur overfitting and suboptimal in some cases. (4) The experimental results on several artificial and benchmark datasets show the effectiveness of our proposed formulation.

Performance analysis of support vector machines classifiers in breast cancer mammography recognition

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.

A Suction Detection System for Rotary Blood Pumps Based on the Lagrangian Support Vector Machine Algorithm

The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided.

On Lagrangian twin support vector regression

In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of inverse of a positive semi-definite matrix that arises in the original formulation of TSVR. The algorithm can be easily implemented and does not need any optimization packages. To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world datasets. Similar or better generalization performance of the proposed method in less training time in comparison with the standard and twin support vector regression methods clearly exhibits its suitability and applicability.

Multiclass Lagrangian support vector machine

A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.