One-class Support Vector Machine Model Research Articles

Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring

A key element of smart manufacturing is condition monitoring and heath controlling of production machines. In today's rapidly evolving landscape of industrial machinery and equipment, optimizing the operation of production lines is critical to ensure high productivity and product quality. Timely detection and prevention of faults in the production process plays a crucial role in minimizing downtime, reducing costs, and ensuring optimal performance. The scientific challenge here is that the increasing number of sensors and actuators with digital input and output signals in the production machines creates different patterns, which are difficult to evaluate using conventional statistical methods. Another difficulty is identifying the cause of the failure to be able to intervene rapidly and in a focused manner in the event of irregularities. For this reason, this research study presented a comprehensive analysis of anomaly detection in binary time series data using various machine learning models. The study included preprocessing of the dataset, normalizing the data, and evaluation of the anomaly detection performance of the different models. The accuracy, detection rate, and F1-score are used as evaluation measures. The execution time of each model is also analyzed. In addition, the identification of sensors that cause anomalies is investigated and the impact of false detections is discussed. Experimental results show the strengths and weaknesses of each model and provide valuable insights for selecting the appropriate anomaly detection approach. The Isolation Forest, Local Outlier Factor, DBSCAN, and kMeans models show high precision and detection, while the Autoencoder and Variational Autoencoder models show high precision but lower detection. The one-class Support Vector Machine model achieves balanced performance. AutoML shows excellent results in recognition rate but is not real-time capable. The results highlight the trade-offs between performance and computational efficiency and point to further research potential in real-time implementation.

The feasibility of using ATR-FTIR spectroscopy combined with one-class support vector machine in screening turmeric powders

Once turmeric has been ground into powder, it is difficult to tell visually if it has been tampered with. In this study, ATR-FTIR spectroscopy was used in tandem with one-class support vector machine (OCSVM) to detect adulteration in turmeric powder. The OCSVM models were trained using 42 pure turmeric powder samples, optimized using 30 pure turmeric powder samples, and subsequently evaluated by classifying 30 pure and 120 adulterated (cornstarch, Metanil Yellow, Orange II, and Sudan I) samples. Preprocessing methods, such as Savitzky-Golay (SG)-derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC), were used individually and in combination to obtain the best-performing model. Models were assessed by comparing the sensitivity, specificity, and efficiency values and compared with one-class soft independent modeling of class analogy (OCSIMCA). The best performing OCSVM model (sensitivity = 1.00, specificity = 0.89) was obtained by first conducting an MSC on the raw data followed by SG-2nd derivative transformation. It also has an efficiency value of 0.94, which was 0.14 higher than when data preprocessing was not done. Compared to the results of OCSIMCA, the OCSVM model gave a higher efficiency value and can detect lower levels of cornstarch adulteration. Also, the results showed that inclusion of data preprocessing can lead to a better classification model. With the obtained evaluation parameter values, ATR-spectroscopy coupled with OCSVM demonstrated its potential for screening turmeric powder products.

Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks

The rapid proliferation of IOT (Internet of Things) networks has brought transformative benefits to industries and everyday life. However, it has also introduced unprecedented cyber security challenges, necessitating advanced techniques for anomaly detection. This research focuses on enhancing cyber security through the application of machine learning-based anomaly detection methods, specifically One-Class Support Vector Machine (SVM) and Isolation Forest, in the context of IOT networks. While Isolation Forest effectively isolates anomalies by building isolation trees, One-Class SVM models the normal data distribution, effectively separating anomalies. To provide a strong security framework for IoT networks, we suggest a comprehensive strategy that combines both algorithms. Our method enables the detection of anomalies in real-time IOT data streams, facilitating prompt responses to new threats. Data collection, preprocessing, and model training are key components. This study helps protect IOT ecosystems and maintain data integrity and privacy in an increasingly connected world by utilizing the benefits of One-Class SVM and Isolation Forest.

NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features.

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer's Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.

FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction

To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation change at each measuring point of the flexible thin-walled structure was completed by ANSYS finite element analysis. The outliers were removed by the OCSVM (one-class support vector machine) model, and the unique mapping relationship between the strain value and the deformation variables (three directions of x-, y-, and z-axis) at each point was completed by a neural-network model. The test results show that the maximum error of the measuring point in the direction of the three coordinate axes: the x-axis is 2.01%, the y-axis is 29.49%, and the z-axis is 15.52%. The error of the coordinates in the y and z directions was large, and the deformation variables were small, the reconstructed shape had good consistency with the deformation state of the specimen under the existing test environment. This method provides a new idea with high accuracy for real-time monitoring and shape reconstruction of flexible thin-walled structures such as wings, helicopter blades, and solar panels.

Vibration data feature extraction and deep learning-based preprocessing method for highly accurate motor fault diagnosis

Abstract The environmental regulations on vessels being strengthened by the International Maritime Organization has led to a steady growth in the eco-friendly ship market. Related research is being actively conducted, including many studies on the maintenance and predictive maintenance of propulsion systems (including electric motors and rotating bodies) in electric propulsion vessels. The present study intends to enhance the artificial intelligence (AI)-based failure-diagnosis rate for electric propulsion vessel propulsion systems. To verify the proposed AI-based failure diagnosis algorithm for electric motors, this study utilized the vibration data of mechanical equipment (electric motors) in an urban railway station. Securing and preprocessing high-quality data is crucial for improving the failure-diagnosis rate, in addition to the performance of the diagnostic algorithm. However, the conventional wavelet transform method, which is generally used for machine signal processing, has a disadvantage of data loss when the data distribution is abnormal or skewed. This study, to overcome this shortcoming, proposes an AI-based denoising auto encoder (DAE) method that can remove noise while maintaining data characteristics for signal processing of mechanical equipment. This study preprocessed vibration data by using the DAE method, and extracted significant features from the data through the feature extraction method. The extracted features were utilized to train the one-class support vector machine model and to allow the model to diagnose the failure. Finally, the F-1 score was calculated by using the failure diagnosis results, and the most meaningful feature extraction method was determined for the vibration data. In addition, this study compared and evaluated the preprocessing performance based on the DAE and the wavelet transform methods.

One class SVM model based on neural tangent kernel for anomaly detection task on small-scale data

Recent studies have shown that the evolution of infinitely wide neural networks satisfying certain conditions can be described by a kernel function called neural tangent kernel (NTK). We introduce NTK into a one-class support vector machine model and select data from different domains in UCI for a small-sample outlier detection task, demonstrate that NTK-OCSVM generally outperforms a variety of commonly used classification models, with more than 20% improvement in accuracy for similar models. When the kernel function parameters are varied, the experiments show that the model has strong robustness within a certain parameter range. Finally, we experimentally compare the time complexity of different models and the decision boundaries, and demonstrate that NTK-OCSVM improves accuracy at the expense of operational efficiency and has linear decision boundaries.

KDE-OCSVM model using Kullback-Leibler divergence to detect anomalies in medical claims

Detecting and preventing unusual claims behavior in medical insurance can reduce the drawdown of a group medical insurance pool. Taking the case of the urban employee basic medical insurance (UEBMI) in China, this paper develops a method to detect unusual medical claim patterns in the UEBMI. We collect public domain records involving gastric malignancy as the experimental sample. A method to preprocess the experimental sample for data learning is provided. We present a feature selection method, involving variance analysis and similarity analysis, to determine the core features. Next, we establish an extended one-class support vector machine (OCSVM) model, the kernel density estimation (KDE)-OCSVM, which exploits the Kullback-Leibler divergence and the KDE method to estimate the parameter v of the OCSVM model, to improve model performance. An experiment and two analyses are performed to validate the proposed method.

Dictionary learning for integration of evidential layers for mineral prospectivity modeling

Machine learning and deep learning anomaly detectors have been successfully used to map mineral prospectivity. However, the establishment of machine learning or deep learning anomaly detection models for mineral prospectivity mapping often requires the determination of a set of initialization parameters in the absence of ground truth data. Improper initialization parameters will degrade the performance of these mineral prospectivity mapping models. Different from most machine learning and deep learning algorithms, dictionary learning is a “white-box” algorithm involving only linear transformations, and building a dictionary learning model requires only an empirical definition of the total number of atoms. Therefore, in this paper, two dictionary learning anomaly detectors were established for mineral prospectivity modeling based on the least angle regression-Lasso (LARS-Lasso) algorithm and the iterative shrinkage-thresholding algorithm (ISTA). The following five-step procedure was proposed for mineral prospectivity modeling using the dictionary learning techniques: (a) an overcomplete dictionary is constructed based on the input data; (b) each data point is transformed into sparse coefficients according to the overcomplete dictionary; (c) the sparse representation of each data point is calculated based on the overcomplete dictionary and the sparse coefficients; and (d) the Euclidean norm of the difference between each data point and its sparse representation is calculated and used as the mineral potential of the data point. The dictionary learning models were established to model gold prospectivity in the Jinchanggouliang district, Inner Mongolia, China, and compared with the logistic regression model and one-class support vector machine model in gold exploration targeting. The result shows that (a) the performances of the dictionary learning models are no less than that of the logistic regression (LGR) model and better than that of the one-class support vector machine (OCSVM) model, and (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. Therefore, the dictionary learning algorithms are high-performance mineral prospectivity modeling techniques. It is worth to further test the effectiveness of the dictionary learning algorithms for different types of mineral deposits in different areas in mineral exploration targeting.

Detection of overdose and underdose prescriptions-An unsupervised machine learning approach.

Overdose prescription errors sometimes cause serious life-threatening adverse drug events, while underdose errors lead to diminished therapeutic effects. Therefore, it is important to detect and prevent these errors. In the present study, we used the one-class support vector machine (OCSVM), one of the most common unsupervised machine learning algorithms for anomaly detection, to identify overdose and underdose prescriptions. We extracted prescription data from electronic health records in Kyushu University Hospital between January 1, 2014 and December 31, 2019. We constructed an OCSVM model for each of the 21 candidate drugs using three features: age, weight, and dose. Clinical overdose and underdose prescriptions, which were identified and rectified by pharmacists before administration, were collected. Synthetic overdose and underdose prescriptions were created using the maximum and minimum doses, defined by drug labels or the UpToDate database. We applied these prescription data to the OCSVM model and evaluated its detection performance. We also performed comparative analysis with other unsupervised outlier detection algorithms (local outlier factor, isolation forest, and robust covariance). Twenty-seven out of 31 clinical overdose and underdose prescriptions (87.1%) were detected as abnormal by the model. The constructed OCSVM models showed high performance for detecting synthetic overdose prescriptions (precision 0.986, recall 0.964, and F-measure 0.973) and synthetic underdose prescriptions (precision 0.980, recall 0.794, and F-measure 0.839). In comparative analysis, OCSVM showed the best performance. Our models detected the majority of clinical overdose and underdose prescriptions and demonstrated high performance in synthetic data analysis. OCSVM models, constructed using features such as age, weight, and dose, are useful for detecting overdose and underdose prescriptions.

Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples

Usually, the One-Class Support Vector Machine (OC-SVM) requires a large dataset for modeling effectively the target class independently to other classes. For finding the OC-SVM model, the available dataset is subdivided into two subsets namely training and validation, which are used for training and validating the optimal parameters. This approach is effective when a large dataset is available. However, when training samples are reduced, parameters of the OC-SVM are difficult to find in absence of the validation subset. Hence, this paper proposes various techniques for selecting the optimal parameters using only a training subset. The experimental evaluation conducted on several real-world benchmarks proves the effective use of the new selection parameter techniques for validating the model of OC-SVM classifiers versus the standard validation techniques

Prediction of fatal traffic accidents using one-class SVMs: a case study in Eskisehir, Turkey

The objective of this study is to investigate the applicability of one-class classification (OCC) models in traffic accident prediction. So far, the accident prediction problem has been considered as a binary classification problem in the literature. Since real accident datasets often involve only accident situations, we thought that OCC could provide more successful predictions. In this study, the fatal accidents, which occurred in Eskisehir, Turkey between 2005 and 2012 was considered. The accidents were tried to be predicted using one-class Support Vector Machine (SVM). In order to compare the performance of the OCC model, some most used binary classifiers were used. Additionally, a non-accident generation procedure was defined to add non-accident cases to the accident dataset. After training, tests were performed using one-class and binary classifiers for the test set generated from the extended dataset. As a result, the one-class SVM model outperformed the binary classification models. Besides, true and false accident alarms were also calculated. The alarm rates obtained with the OCC model also demonstrated that OCC can be suitable for accident prediction rather than binary classification.

Human-Like Posture Correction for Seven-Degree-of-Freedom Robotic Arm

Abstract Owing to advancements in robotics, researchers have been focusing on integrating humanoid robots into actual environments. Most humanoid robots are equipped with seven-degree-of-freedom (DoF) arms that allow them to be flexible in different scenarios. The controller of a 7-DoF robotic arm must select one solution among the infinite sets of solutions for a given inverse kinematics problem. To date, no suitable approach has been developed for identifying appropriate human-like postures for a robotic arm with an offset wrist configuration. In this paper, we propose a novel algorithm that considers the movement of the human arm to consistently find a suitable human-like posture. First, a one-class support vector machine model is employed to classify human-like postures. Then, the algorithm uses the redundancy characteristic of a 7-DoF robotic arm with a linear regression model to enhance the search of human-like postures. Finally, the proposed algorithm is demonstrated in simulation, where it successfully optimized point-to-point trajectories by modifying only the endpoint posture.

Efficient Anomaly Detection for High-Dimensional Sensing Data With One-Class Support Vector Machine

This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.

Machine learning for anomaly detection in cyanobacterial fluorescence signals

Many drinking water utilities drawing from waters susceptible to harmful algal blooms (HABs) are implementing monitoring tools that can alert them to the onset of blooms. Some have invested in fluorescence-based online monitoring probes to measure phycocyanin, a pigment found in cyanobacteria, but it is not clear how to best use the data generated. Previous studies have focused on correlating phycocyanin fluorescence and cyanobacteria cell counts. However, not all utilities collect cell count data, making this method impossible to apply in some cases. Instead, this paper proposes a novel approach to determine when a utility needs to respond to a HAB based on machine learning by identifying anomalies in phycocyanin fluorescence data without the need for corresponding cell counts or biovolume. Four widespread and open source algorithms are evaluated on data collected at four buoys in Lake Erie from 2014 to 2019: local outlier factor (LOF), One-Class Support Vector Machine (SVM), elliptic envelope, and Isolation Forest (iForest). When trained on standardized historical data from 2014 to 2018 and tested on labelled 2019 data collected at each buoy, the One-Class SVM and elliptic envelope models both achieve a maximum average F1 score of 0.86 among the four datasets. Therefore, One-Class SVM and elliptic envelope are promising algorithms for detecting potential HABs using fluorescence data only.

Anomaly Detection for Industrial Control Networks Based on Improved One-Class Support Vector Machine

In traditional network anomaly detection algorithms, the anomaly threshold needs to be defined manually. Keeping this as background, this study proposes an anomaly detection algorithm (VAEOCSVM), which combines the variable auto-encoder (VAE) and one-class support vector machine (OCSVM) to realize anomaly detection in industrial control networks. First, the VAE model is used to obtain the distribution of the original normal sample data represented by the low-dimensional code; the reconstruction error of the VAE model is merged into the new input. Then, using OCSVM’s hinge-loss objective function and the random Fourier feature fitting radial basis function (RBF) kernel method, the OCSVM model is represented and solved using the deep neural network and gradient descent method. Finally, the decision function of the OCSVM model is constructed by using the solved parameter information to realize the detection of abnormal data. The proposed algorithm is compared with other machine-learning-based anomaly detection algorithms in terms of multiple indicators such as precision, recall, and [Formula: see text] score. The experimental results using various datasets show that the proposed algorithm has a better outlier recognition ability than the machine-learning-based anomaly detection algorithms.

A hybrid data-driven fault detection strategy with application to navigation sensors

The integrated navigation system highly relies on the accuracy of measurements of sensors that are susceptible to unknown disturbances. In order to improve the reliability and safety of the navigation system, there is an increasing need for the fault detection of the sensors. In the present study, a hybrid data-driven fault detection strategy is proposed, which is based on residual sequence analysis. Currently, the one-class support vector machine is one of the most popular fault detection methods for navigation systems with many successful cases. Therefore, the one-class support vector machine is combined with time-series similarity measure and modified principal components analysis approaches. The similarity measurement of multi-sequence residuals between a real-time sample and normal condition samples is computed to construct learning features for one-class support vector machine. Similarly, the modified principal components analysis scheme is applied to project residuals onto subspaces and obtain learning features. Moreover, the one-class support vector machine model is applied for abnormal detection if unexpected sensor faults exhibit in measurements and residuals. Finally, experiments are carried out to evaluate the performance of the proposed strategy for abrupt faults and soft faults on navigation sensors. Experimental results show that the hybrid data-driven fault detection strategy can effectively detect these faults with short time delay and high accuracy.

Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine

The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links between geochemical characteristics and mineralization. However, this method faces the issue of data redundancy and calculation complexity, and high-dimensional problems raise great challenges for anomaly detection. This is the curse of dimensionality problem, which hinders the development of a variety of techniques for anomaly detection. In this study, a hybrid model that combines unsupervised deep belief networks (DBNs) and one-class support vector machine (OCSVM) is adopted to address the high-dimensional geochemical anomalies detection problem. In this model, the relevant features first extracted through the DBN are used as the input of the OCSVM. The decision function values of the hybrid method are employed to map the geochemical patterns related to iron mineralization. The comparative results on the performance of the hybrid model and the other three anomaly detection models (deep autoencoder model, OCSVM, and hybrid model with principal component analysis and OCSVM) in terms of the area under curve(AUC) values, suggest that the hybrid method of the DBN and OCSVM can efficiently recognize the geochemical anomalies related to iron mineralization. The DBN can extract the geochemical information, reduce the redundant features, and further enhance the scalability of the OCSVM for processing high-dimensional geochemical data. The extracted geochemical anomalies, which show a close spatial relationship with the Yanshanian intrusions, can provide significant guidance for the next round of mineral exploration.

Predictive Modeling of Antibiotic Susceptibility in E. Coli Strains Using the U-Net Network and One-Class Classification

The antibiotic resistance of bacterial pathogens has become one of the most serious global health issues due to misusing and overusing of antibiotics. Recently, different technologies were developed to determine bacteria susceptibility towards antibiotics; however, each of these technologies has its advantages and limitations in clinical applications. In this contribution, we aim to assess and automate the detection of bacterial susceptibilities towards three antibiotics; i.e. ciprofloxacin, cefotaxime and piperacillin using a combination of image processing and machine learning algorithms. Therein, microscopic images were collected from different E. coli strains, then the convolutional neural network U-Net was implemented to segment the areas showing bacteria. Subsequently, the encoder part of the trained U-Net was utilized as a feature extractor, and the U-Net bottleneck features were utilized to predict the antibiotic susceptibility of E. coli strains using a one-class support vector machine (OCSVM). This one-class model was always trained on images of untreated controls of each bacterial strain while the image labels of treated bacteria were predicted as control or non-control images. If an image of treated bacteria is predicted as control, we assume that these bacteria resist this antibiotic. In contrast, the sensitive bacteria show different morphology of the control bacteria; therefore, images collected from these treated bacteria are expected to be classified as non-control. Our results showed 83% area under the receiver operating characteristic (ROC) curve when OCSVM models were built using the U-Net bottleneck features of control bacteria images only. Additionally, the mean sensitivities of these one-class models are 91.67% and 86.61% for cefotaxime and piperacillin; respectively. The mean sensitivity for the prediction of ciprofloxacin is only 59.72% as the bacteria morphology was not fully detected by the proposed method.

Intrusion Detection of Industrial Control System Based on Double-layer One-class Support Vector Machine

In this paper, the stealthy attack detection in industrial control system (ICS) is studied, and a new detection method is proposed from the perspective of signal analysis. The method consists of a double-layer one-class support vector machine model (DL-OCSVM), where the first-layer model is the standard OCSVM, and the second-layer model is obtained by incremental learning based on the former. The wavelet decomposition is used to extract the physical characteristics of the ICS. The KKT condition and the adjacent classification interval are adopted to reduce the training samples, improving the learning rate and system scalability. In addition, the designed boundary samples are employed for incremental learning, avoiding overfitting and reducing false positives rate (FPR). The experimental results show that the proposed method has high detection rate and low FPR for stealthy attacks, and is more suitable for precision machining process.