One-class Classification Method Research Articles

Advancing fraud detection in New Zealand Mānuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification

New Zealand Mānuka honey has become a prime target for adulteration due to its high commercial value. Given the diverse possibilities of fraudulent activities, training a supervised model that exhaustively covers all potential fraud scenarios is challenging. This study presents a new method for detecting fraudulent behavior in New Zealand Mānuka honey by combining hyperspectral imaging (HSI) with the GANomaly-based One-Class Classification method. We collected 18 different UMF-graded pure Mānuka honey samples from five New Zealand brands, which were used for training. The model was tested on fraudulent honey, including aged and syrup-adulterated honey, and compared with the traditional One-Class Classification methods. The results demonstrate that the HSI combined with the GANomaly method achieved 100% discrimination for all test samples, outperforming the standard rival techniques. In conclusion, this research developed a versatile model capable of detecting honey fraudulent behavior, showing significant practical implications for honey quality assessment.

Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression.

This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts.

Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment

Fault detection or anomaly detection relies heavily on learning from datasets where only normal samples are available, resulting in the emergence of numerous one-class classification (OCC) methods. However, learning discriminative deep representatives with good generalization from cross-domain positive samples remains challenging. Therefore, this work proposes an end-to-end framework, deep transfer one-class classification (DTOCC) for unsupervised fault detection, which combines adversarial generative OCC and distribution alignment from the perspective of manifold learning. Specifically, pseudo-negative samples are generated outside the positive manifold, facilitating the model to learn discrimination with respect to normal and anomaly. Further, cross-domain positive samples are aligned in log-Euclidean manifold space to enhance representation learning. Then, we provide the specific implementations for fault detection and validate its superiority through case studies on multi-class and run-to-failure datasets, simulating both offline and online scenarios.

Robust one-class classification using deep kernel spectral regression

The existing one-class classification (OCC) methods typically presume the existence of a pure target training set and generally face difficulties when the training set is contaminated with non-target objects. This work addresses this aspect of the OCC problem and formulates an effective method that leverages the advantages of kernel-based methods to achieve robustness against training label noise while enabling direct deep learning of features from the data to optimise a Fisher-based loss function in the Hilbert space. As such, the proposed OCC approach can be trained in an end-to-end fashion while, by virtue of a Tikhonov regularisation in the Hilbert space, it provides high robustness against the training set contamination.Extensive experiments conducted on multiple datasets in different application scenarios demonstrate that the proposed methodology is robust and performs better than the state-of-the-art algorithms for OCC when the training set is corrupted by contamination.

Water distribution pipe lifespans: Predicting when to repair the pipes in municipal water distribution networks using machine learning techniques

Water is one of the essential matters that keeps living species alive; yet, the lifespan of pipes has two direct impacts on wasting water in very great amounts: pipe leakages and pipe bursts. Consequently, the proper detection of aged pipes in the water distribution networks has always been an issue in overcoming the problem. This makes water pipe monitoring an important duty of municipalities. Traditionally, leakages and bursts were only detected visually or through reports in local areas, leading municipalities to change the old pipes. Although this helps to fix the issue, a more desired way is to perhaps let officials know about the possibilities of such problems in advance by predicting which pipes are aged, so they can prevent the wastage. Therefore, to automate the detection process, in this study, we take the initial steps to predict the pipes needing repair in a particular area using machine learning methods. We first obtain a private dataset provided by the municipality of Saveh, Iran which outlines pipes that were damaged previously. We then train three machine learning algorithms to predict whether a set of pipes in an area is prone to damage. To achieve this, One-Class (OC) Classification methods such as OC-SVM, Isolation Forest, and Elliptic Envelope are used and they achieved the highest accuracy of 0.909. This study is of value since it requires zero additional devices (i.e., sensors).

Locally Masked Convolutional Models for Anomalous Sound Detection by Deep Learning

Anomalous Sound Detection by Deep Learning could be used to automatically detect malfunctions in mechanical products from their operating sounds. With many normal sounds but very few anomalous sounds available for training, one-class classification methods applied to sound spectrograms are the most suitable methods. Among them, the Masked Autoencoder for Distribution Estimation (MADE) method showed state-of-the-art results with transient anomalous sounds but poor results with sustained anomalous sounds. The loss of the spectro-temporal structure in the inputs possibly explaining this issue, we have modified a method recently designed for 2D image completion, taking advantage of the full spectro-temporal structure of the inputs. When evaluated on industrial data, this new method outperforms a baseline Autoencoder method and the original MADE method.

Privileged multi-view one-class support vector machine

One-class support vector machine (OCSVM) is a typical one-class classification approach, which learns the classifier by using only the target samples. At present, most OCSVM works hypothesize that the samples have only one view, while multi-view OCSVM has not been taken into account. In this paper, a novel multi-view one-class support vector machine method with privileged information learning (MOCPIL) is put forward. MOCPIL embodies both the consensus principle and complementarity principle in multi-view learning. Privileged information is additional data that is available only in the training process, but not in the testing process. By introducing the idea of privileged information learning, MOCPIL implements the complementarity principle by treating one view as the training data and the other view as the privileged data. Moreover, MOCPIL implements the consensus principle by requiring that different views of the same object should give similar predicting outputs. The learning problem of MOCPIL is a quadratic programming (QP) problem, which is able to be solved by off-the-shelf QP solvers. To the best of our knowledge, this is the first study to tackle the multi-view learning problem based on OCSVM. The performance of MOCPIL is evaluated through extensive experiments. The experimental results have shown that MOCPIL explicitly outperforms the existing multi-view one-class classification methods.

Machinery Fault Signal Detection with Deep One-Class Classification

Fault detection of machinery systems is a fundamental prerequisite to implementing condition-based maintenance, which is the most eminent manufacturing equipment system management strategy. To build the fault detection model, one-class classification algorithms have been used, which construct the decision boundary only using normal class. For more accurate one-class classification, signal data have been used recently because the signal data directly reflect the condition of the machinery system. To analyze the machinery condition effectively with the signal data, features of signals should be extracted, and then, the one-class classifier is constructed with the features. However, features separately extracted from one-class classification might not be optimized for the fault detection tasks, and thus, it leads to unsatisfactory performance. To address this problem, deep one-class classification methods can be used because the neural network structures can generate the features specialized to fault detection tasks through the end-to-end learning manner. In this study, we conducted a comprehensive experimental study with various fault signal datasets. The experimental results demonstrated that the deep support vector data description model, which is one of the most prominent deep one-class classification methods, outperforms its competitors and traditional methods.

Proximity-based density description with regularized reconstruction algorithm for anomaly detection

This study addresses unsupervised anomaly detection using one-class classification, which constructs a decision boundary to determine if a new instance belongs to the target class. Existing one-class classification methods often fail in real-world scenarios due to their sensitivity to noise and inability to handle complex structures. We propose a proximity-based density description with a regularized reconstruction algorithm to overcome these limitations. Our method defines density-descriptive coefficients to reconstruct initial density and derives optimal coefficients by minimizing reconstruction error subject to sparsity and smoothness constraints. The sparsity constraint reduces noise effects, while the smoothness constraint encourages a flexible decision boundary. We evaluate our algorithm on benchmark datasets and compare it to existing methods, demonstrating superior performance.

Limit of detection in qualitative analysis: Classification Analytical Signal approach

This paper presents a new methodology for the estimation of the limit of detection and other related parameters in qualitative analysis. Estimation of the detection limit is important in special cases when a small amount of an illegal ingredient can significantly affect the quality of the product or the presence of even a small amount of an extraneous analyte is significant. The approach is based on a recently introduced concept of an analytical signal in qualitative analysis, called the Classification Analytical Signal (CAS). A detailed algorithm to calculate the detection capability for a one-class classification method Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) is described and illustrated with two real-world examples. Both examples consider cases where the detection of low analyte concentrations is important and corresponding samples should be considered as non-compliant. The first case presents adulteration of wheat flour with talcum powder, where the samples were specially prepared with various concentrations of the analyte. The second case uses a publicly available dataset devoted to the estimation of tetracycline in blood serum. The latter case is used to compare the limits of detection estimated for multivariate calibration with those presented for qualitative analysis.

Geographical origin identification of camellia oil based on fatty acid profiles combined with one-class classification

Geographical Indication (GI) agricultural products possess specific geographical origins and high qualities, which require an effective geographical origin traceability method for the important protective trademarks. In this study, authentication models for Changshan camellia oil were developed by fatty acid profiles and one-class classification methods including data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OCPLS), and compared with traditional two-class classification models. The results indicated that the prediction errors of three two-class classification models were 63.8%, 12.1%, and 65.2% for the samples out of targeted geographical origins, respectively. By contrast, the one-class classification models could completely differentiate Changshan from non-Changshan camellia oils, even from the adjacent counties. Moreover, compared with traditional indicators of mineral elements, the model built by fatty acid profiles possessed higher sensitivity and specificity. It also offered a reference strategy for the geographical origin identification of other high-value oils or foods.

Rapid and nondestructive identification of adulterate capsules by NIR spectroscopy combined with chemometrics

This study aims to develop a rapid and non-destructive method to identify counterfeit and substandard drugs, addressing the critical need for better quality control in drug production. According to the reasons for counterfeit products in actual production, the commonly used solid preparation excipients such as HPMC, MCC, Mg-St and Pregelatinized Starch, as well as three chemical drugs with similar efficacy to Guizhi-Fuling (GZFL) Capsule as adulterants, including Aspirin, Ibuprofen and Sinomenine Hydrochloride were selected and designed as adulteration samples with different levels of adulteration. NIR spectra were collected in a non-invasive mode and analyzed by one-class classification methods. The feasibility of using Near-infrared (NIR) spectroscopy as a detection method to qualitatively identify adulterated samples was explored at three packaging levels of powder, intact capsules and capsules in PVC. The differences between the samples were analyzed by NIR spectra comparison, cluster analysis and principal component analysis. The performance of SVM, OCPLS and DD-SIMCA models in dealing with the authentication of genuine and counterfeit products was established and compared. The results show that the spectra contain sample information and the adulterated samples could be discriminated correctly by established models. Moreover, applying appropriate spectral preprocessing methods can further improve the model's performance. In addition, a PLS regression model was developed to predict the adulteration levels of the three packing level samples, which yielded satisfactory results. This study highlights the potential of NIR spectroscopy combined with Chemometrics as a rapid and non-destructive testing analysis method to accurately identify counterfeit and substandard drugs, thereby ensuring drug quality.

On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles

It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56–64, 2009; Janssens et al. in: Proceedings of the 2009 ICMLA international conference on machine learning and applications, IEEE Computer Society, pp 147–153, 2009. https://doi.org/10.1109/ICMLA.2009.16). In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. In contrast to previous comparison studies, where the models (algorithms, parameters) are selected by using examples from both classes (outlier and inlier), here we also study and compare different approaches for model selection in the absence of examples from the outlier class, which is more realistic for practical applications since labeled outliers are rarely available. Our results showed that, overall, SVDD and GMM are top-performers, regardless of whether the ground truth is used for parameter selection or not. However, in specific application scenarios, other methods exhibited better performance. Combining one-class classifiers into ensembles showed better performance than individual methods in terms of accuracy, as long as the ensemble members are properly selected.

Clustering ensemble-based novelty score for outlier detection

Recently, One-class classification algorithms have been successfully used for outlier detection problems in several industrial fields. However, in case of that the target class has complex structures, single outlier detection model with one-class classifier often poorly performs because it cannot appropriately reflect intrinsic data structures. To address this limitation, we propose a clustering ensemble-based novelty score algorithm. The proposed algorithm calculates novelty score from the mixture of multiple clustering solutions generated by both random subspace and random-K ensemble approaches. Then, final ensemble novelty score is defined by summarizing multiple novelty scores obtained from individual clustering results. Because these multiple novelty scores are computed from many possible characteristics of target class information, the proposed ensemble novelty score can appropriately reflect the inherent structures of target class. Experiments were conducted on various benchmark datasets to compared with existing methods and investigate the properties of the proposed algorithm. The experimental results confirm that the proposed algorithm outperforms existing one-class classification methods in various cases.

One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging

We investigated a method for automatic skin tissue characterization based on optical coherence tomography (OCT) imaging. We developed a manually scanned single fiber OCT instrument to perform in vivo skin imaging and tumor boundary assessment. The goal is to achieve more accurate tissue excision in Mohs micrographic surgery (MMS) and reduce the time required for MMS. The focus of this study was to develop a novel machine learning classification method to automatically identify abnormal skin tissues through one-class classification. We trained a deep convolutional neural network (CNN) with a U-Net architecture for automatic skin segmentation, used the pre-trained U-Net as a feature extractor, and trained one-class support vector machine (SVM) classifiers to detect abnormal tissues. The novelty of this study is the use of a neural network as a feature extractor and the use of a one-class SVM for abnormal tissue detection. Our approach eliminated the need to engineer the features for classification and eliminated the need to train the classifier with data obtained from abnormal tissues. To validate the effectiveness of the one-class classification method, we assessed the performance of our algorithm using computer synthesized data, and experimental data. We also performed a pilot study on a patient with skin cancer.

Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

This article studies remote sensing image retrieval using kernel-based support vector data description (SVDD). We exploit deep SVDD, which is a well-known method for one-class classification to recover the most relevant samples from the archive. To this end, a deep neural network (DNN) is jointly trained to map the data into a hypersphere of minimum volume in the latent space. It is expected that similar samples to the query are compressed inside of the hypersphere. The closest embedding to the center of the hypersphere is related to the most relevant sample to query. We enhance deep SVDD by injecting the statistical information of data to the DNN by means of additional terms in the cost function. The first enhancement method takes advantage of covariance regularization of batches of the training set to penalize unnecessary redundancy and minimize the correlation between the different dimensions of the embedding. The second method involves unlocking the hypersphere's predefined center while preventing network divergence during training. Therefore, two parameters are designed to control the importance of the drifting of the center and the importance of a fixed predefined center (convergence), respectively. This has been implemented by considering the average of batches of embedding in each iteration as the updated center. This pushes irrelevant samples away from query samples, making data clustering easier for the DNN. The performance of the proposed methods is evaluated on benchmark datasets.

Development of one-class classification method for identifying healthy T. granosa from those contaminated with uncertain heavy metals by LIBS

Development of one-class classification method for identifying healthy T. granosa from those contaminated with uncertain heavy metals by LIBS

Rapid identification of healthy Tegillarca granosa using laser-induced breakdown spectroscopy and fusion model

Abstract Objectives This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry (LIBS) to quickly identify healthy Tegillarca granosa (T. granosa). Materials and Methods The sum of ranking differences (SRD) was used to fuse multiple anomaly detection metrics to build the one-class classifier, which was only trained with healthy T. granosa. The one-class classifier can identify healthy T. granosa to exclude non-healthy T. granosa. The proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion matrix. Based on the fusion matrix, the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be unhealthy. Results Multiple anomaly detection metrics were fused by the SRD algorithm and tested on each band, and the final fusion model achieved an accuracy rate of 98.46%, a sensitivity of 100%, and a specificity of 80%. The remaining three single classification models obtained the following results: the SVDD model achieved an accuracy rate of 87.69%, a sensitivity of 90%, and a specificity of 60%; the OCSVM model achieved an accuracy rate of 80%, a sensitivity of 76.67%, and a specificity of 60%; and the DD-SIMCA model achieved an accuracy rate of 95.38%, a sensitivity of 98.33%, and a specificity of 60%. Conclusions The experimental results showed that the proposed method achieved better results than the traditional one-class classification methods with a single metric. Therefore, the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances (healthy T. granosa).

Automated Detection Method to Extract Pedicularis Based on UAV Images

Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of multi-class classifiers is better than that of one-class classifiers, but it requires all classes that occur in the image to be exhaustively assigned labels. Among the one-class classifiers that only need to label positive or positive and labeled data, the PUL has the highest F score of 0.9878. (2) PUL performs the most robustly to change features in one-class classifiers. All one-class classifiers prove that the green band is essential for extracting Pedicularis. (3) The parameters of the PUL are easy to tune, and the training time is easy to control. Therefore, PUL is a promising one-class classification method for Pedicularis extraction, which can accurately identify the distribution range of Pedicularis to promote grassland administration.

A Hybrid Algorithm Incorporating Vector Quantization and One-Class Support Vector Machine for Industrial Anomaly Detection

Anomaly detection plays an important role in industry, especially in ensuring system safety and product quality. Due to the unavailability of anomalous data in many practical cases, anomaly detection is usually solved by one-class classification (OCC) methods using only normal data. As a classical OCC method, one-class support vector machine (OCSVM) is a popular discriminative approach for anomaly detection, which detects abnormal data points by establishing a decision boundary in the kernel space. However, the performance of OCSVM heavily relies on kernel parameters, whose selection is not trivial for anomaly detection problems. Moreover, for some uneven and complex data distributions, different data regions may have quite different densities and shapes, making it difficult for OCSVM to obtain good boundaries in all regions using a global kernel parameter. To address the above two issues, in this article, we propose a hybrid algorithm incorporating vector quantization and OCSVM (VQ-OCSVM). Specifically, vector quantization is used to extract distribution information of normal data, and the results are used to construct an explicit mapping function to map data into a high-dimensional feature space. Then, OCSVM is performed in the feature space to build a classifier. By introducing the explicit mapping into OCSVM, the proposed method can effectively bypass the kernel parameter selection problem of the classical OCSVM method. Furthermore, the constructed mapping carries the data distribution information, and the VQ-OCSVM model can be regarded as an integration of generative learning and discriminative learning. The complementary properties of these two paradigms make the proposed VQ-OCSVM algorithm have better generalization capacity for complex data distribution. Both qualitative and quantitative experimental results demonstrate the effectiveness and advantages of the proposed method.