One-class Classification Research Articles

Method for removing haze from images, captured under a wide range of lighting conditions

The presence of haze on images degrades the quality of perception and automatic analysis of scenes. One of the most popular methods of haze removal is the dark channel prior method, which is based on the Koschmieder atmospheric scattering model. However, its underlying assumptions are not met for nighttime, since localized light sources make a significant, if not the main, contribution to lighting. We propose here to use the degree of belonging of an image element to a localized light source, determined based on a one-class classifier, as a value that characterizes the confidence of the corresponding element of the estimated transmission map during its rectifi-cation based on the gamma-normal model, which makes it possible to increase the accuracy of dehazing when processing images, captured in low-light or nighttime conditions.

Quantum variational distance-based centroid classifier

Quantum machine learning is a rapidly growing field with the potential to surpass classical methods and offer novel solutions to learning problems. Supervised classification, a prominent pattern recognition task, has attracted significant attention for improving existing methods using quantum techniques. Although previous quantum algorithms for distance-based classification have shown promise, they are not compatible with current Noisy Intermediate-Scale Quantum (NISQ) devices and cannot handle multiclass problems. To address these limitations, we present the quantum Variational Distance-based Centroid Classifier (qVDCC), a hybrid approach that combines the Quantum One-Class Classifier (QOCC) and Parameterized Quantum Circuits (PQCs). This approach greatly improves the feasibility of multiclass classification on NISQ devices, as it eliminates the need for a label qubit and uses a single-qubit measurement, regardless of data size. Our simulations on seven benchmark datasets using both noisy and noise-free simulations show that qVDCC is competitive with the k-nearest neighbors algorithm, while providing exponential data compression and efficient quantum data processing.

Simple knowledge graph completion model based on PU learning and prompt learning

Simple knowledge graph completion model based on PU learning and prompt learning

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).

Interpretable synthetic signals for explainable one-class time-series classification

This research paper introduces an innovative approach for explainable one-class time-series classification (XOCTSC). The proposed method involves generating pseudounseen synthetic signals by altering the amplitude and cycle of the original signals. Subsequently, a classification process is performed to distinguish between the original and synthetic signals, and the resulting model is applied to testing data. Instances classified as synthetic classes are treated as unseen classes, and the dissimilarity with the training data can be elucidated through an explanation of the synthetic class creation process. This approach aims to enhance the interpretability of one-class time-series classification models by providing insights into the reasoning behind their decisions. The proposed method is demonstrated with a ballistocardiogram (BCG) signal for the breathing dataset and an electroencephalogram (EEG) signal for the epilepsy dataset. The proposed method recognizes BCG amplitude reduction during breath holding. Moreover, EEG cycle changes during epileptic seizures are observed across multiple channels. These observations align with actual epilepsy symptoms and breathing behaviors.

Nonintrusive Load Identification Considering Unknown Load Based on Bimodal Fusion and One-Class Classification

Nonintrusive Load Identification Considering Unknown Load Based on Bimodal Fusion and One-Class Classification

Deep Optimized Broad Learning System for Applications in Tabular Data Recognition.

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data. However, the rapid expansion of big data has resulted in an overwhelming amount of tabular data, necessitating the development of specialized tools for effective management and analysis. This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis. In addition, a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS. The main contributions of this article are: 1) by retracing the network's error from the output space to the latent space, the OBLS adjusts parameters in the feature and enhancement node layers. This process aims to achieve more resilient representations, resulting in improved performance; 2) the DOBLS is a multilayered structure consisting of multiple OBLSs, wherein each OBLS connects to the input and output layers, enabling direct data propagation. This design helps reduce information loss between layers, ensuring an efficient flow of information throughout the network; and 3) the proposed methods demonstrate robustness across various applications, including multiview feature embedding, one-class classification (OCC), camera model identification, electroencephalogram (EEG) signal processing, and radar signal analysis. Experimental results validate the effectiveness of the proposed models. To ensure reproducibility, the source code is available at https://github.com/1027051515/OBLS_DOBLS.

Consistency and Complementarity Jointly Regularized Subspace Support Vector Data Description for Multimodal Data

The one‐class classification (OCC) problem has always been a popular topic because it is difficult or expensive to obtain abnormal data in many practical applications. Most of OCC methods focused on monomodal data, such as support vector data description (SVDD) and its variants, while we often face multimodal data in reality. The data come from the same task in multimodal learning, and thus, the inherent structures among all modalities should be hold, which is called the consistency principle. However, each modality contains unique information that can be used to repair the incompleteness of other modalities. It is called the complementarity principle. To follow the above two principles, we designed a multimodal graph–regularized term and a sparse projection matrix–regularized term. The former aims to preserve the within‐modal structural and between‐modal relationships, while the latter aims to richly use the complementarity information hidden in multimodal data. Further, we follow the multimodal subspace (MS) SVDD architecture and use two regularized terms to regularize SVDD. Consequently, a novel OCC method for multimodal data is proposed, called the consistency and complementarity jointly regularized subspace SVDD (CCS‐SVDD). Extensive experimental results demonstrate that our approach is more effective and competitive than other algorithms. The source codes are available at https://github.com/wongchuang/CCS_SVDD.

Malware Detection With Subspace Learning-Based One-Class Classification

Malware Detection With Subspace Learning-Based One-Class Classification

Classification of Dry and Wet Periods Using Commercial Microwave Links: A One-Class Classification Machine Learning Approach Based on Autoencoders

Classification of Dry and Wet Periods Using Commercial Microwave Links: A One-Class Classification Machine Learning Approach Based on Autoencoders

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.

Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection.

Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending event detection in video surveillance to finding lesions in medical scans. Regardless of the domain, anomaly detection is typically framed as a one-class classification task, where the learning is conducted on normal examples only. An entire family of successful anomaly detection methods is based on learning to reconstruct masked normal inputs (e.g. patches, future frames, etc.) and exerting the magnitude of the reconstruction error as an indicator for the abnormality level. Unlike other reconstruction-based methods, we present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. The proposed self-supervised block is extremely flexible, enabling information masking at any layer of a neural network and being compatible with a wide range of neural architectures. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss. Furthermore, we show that our block is applicable to a wider variety of tasks, adding anomaly detection in medical images and thermal videos to the previously considered tasks based on RGB images and surveillance videos. We exhibit the generality and flexibility of SSMCTB by integrating it into multiple state-of-the-art neural models for anomaly detection, bringing forth empirical results that confirm considerable performance improvements on five benchmarks: MVTec AD, BRATS, Avenue, ShanghaiTech, and Thermal Rare Event.

Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models

The maximum fraction difference (MFD) method for evaluating OCC model performance without needing negative data for validation and its implementation in MOFs synthesisability prediction.

Learning peptide properties with positive examples only.

Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.

Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network

Fake news detection has gotten continuous attention during these years with more and more people have been posting and reading news online. To enable fake news detection, existing researchers usually assume labeled posts are provided for two classes (true or false) so that the model can learn a discriminative classifier from the labeled data. However, this supposition may not hold true in reality, as most users may only label a small number of posts in a single category that they are interested in. Furthermore, most existing methods fail to mask the noise or irrelevant context (i.e., regions or words) between different modalities to assist in strengthening the correlations between relevant contexts. To tackle these issues, we present a curriculum-based multi-modal masked transformer network (CMMTN) for positive unlabeled multi-modal fake news detection by jointly modeling the inter-modality and intra-modality relationships of multi-modal information and masking the irrelevant context between modalities. In particular, we adopt BERT and ResNet to obtain better representations for texts and images, separately. Then, the extracted features of images and texts are fed into a multi-modal masked transformer network to fuse the multi-modal content and mask the irrelevant context between modalities by calculating the similarity between inter-modal contexts. Finally, we design a curriculum-based PU learning method to handle the positive and unlabeled data. Massive experiments on three public real datasets prove the effectiveness of the CMMTN.

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.

A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS)

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development

We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD). However, pre-trained off-the-shelf CNN representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. To adapt representations to relevant details in healthy tissue we propose training a CNN on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. Almost no additional labeling workload is required, since healthy samples come automatically with aforementioned labels. During training we enforce compact image representations with a center-loss term, which further improves representations for AD. The proposed system outperforms established AD methods on a published dataset of liver anomalies. Moreover, it provided comparable results to conventional methods specifically tailored for quantification of liver anomalies. We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.