Outlier Score Research Articles

A novel LDVP-based anomaly detection method for data streams

Recently, some progress has been made in anomaly detection for data streams. However, these methods still exhibit deficiencies in effectively balancing computational efficiency and detection accuracy. In this paper, we propose a novel anomaly detection method called Dynamic Window Anomaly Detection based on Local Density of Vector dot Product (DWAD-LDVP) for data streams. DWAD-LDVP uses an adaptive dynamic sliding window mechanism with a verification model to balance the relationship between computational efficiency and detection accuracy. By converting local density values measured by the vector dot product into outlier scores, DWAD-LDVP can improve the accuracy of anomaly detection compared to SOTA methods. Furthermore, an incremental computation module is adopted to process data streams efficiently, thereby reducing unnecessary computational overhead. Simulation experiments on synthetic data and real-world datasets validate the robustness and superior performance of the proposed DWAD-LDVP method. Specifically, the experimental results indicate that the proposed DWAD-LDVP achieves competitive performance measured by accuracy, precision, recall, F1 score, and ROC AUC, confirming its effectiveness in anomaly detection tasks.

Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning

Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization’s network systems and understand its structure and security procedures, making it difficult to detect this type of behavior through traditional behavioral auditing. This paper proposes a method to leverage unsupervised outlier scores to enhance supervised insider threat detection by integrating the advantages of supervised and unsupervised learning methods and using multiple unsupervised outlier mining algorithms to extract from the underlying data useful representations, thereby enhancing the predictive power of supervised classifiers on the enhanced feature space. This novel approach provides superior performance, and our method provides better predictive power compared to other excellent abnormal detection methods. Using only 20% of the computing budget, our method achieved an accuracy of 86.12%. Compared with other anomaly detection methods, the accuracy increased by up to 12.5% under the same computing budget.

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Because of its close relationship with information technology, every change in the field of information technology will have an important impact on the means, time and space distribution of the final fraud of the credit card network. The rapid development and popularization of artificial intelligence (AI) software based on Deepfake technology has greatly promoted the intelligent transformation of credit card network fraud. Through the use of advanced hardware and software equipment, criminals have iterated on their own fraud tools, from the use of traditional phone calls and text messages to the use of the latest AI software. Therefore, for the problem of credit card fraud detection with missing data set labels and highly unbalanced category distribution, A credit card approval anomaly detection model DES-HBOS (Dynamic Ensemble Selection based on Histogram-based Outlier Score) is proposed. Firstly, the unsupervised anomaly detection algorithm is used to construct the false label of the training set customer. Then, the customer capability area to be measured is determined, and the classifier performance is evaluated according to Pearson correlation coefficient. Finally, a set of optimal classifiers is selected to integrate the test customers. Experiments on real credit card customer data sets show that DES-HBOS has a higher Recall and can identify more fraudulent customers than other 6 classical anomaly detection models. Comparative experiments were carried out on 4 unbalanced data sets, and the experimental results showed that DES-HBOS had stronger anomaly detection ability than HBOS.

Investigating Outlier Detection Techniques Based on Kernel Rough Clustering

Background: Data quality is crucial to the success of big data analytics. However, the presence of outliers affects data quality and data analysis. Employing effective outlier detection techniques to eliminate dirty data can improve data quality and garner more accurate analytical insights. Data uncertainty presents a significant challenge for outlier detection methods and warrants further refinement in the era of big data. Objective: The unsupervised outlier detection based on the integration of clustering and outlier scoring scheme is the current research hotspot. However, hard clustering fails when dealing with abnormal patterns with uncertain and unexpected behavior. Rough boundaries help identify more accurate cluster structures. Therefore, this article uses uncertainty soft clustering based on rough set theory to extend the clustering technology and designs appropriate scoring schemes to capture abnormal instances. This solves the problem of outlier detection in uncertain and nonlinear complex data. Methods: This paper proposes the flow of an outlier detection algorithm based on Kernel Rough Clustering and then compares the detection accuracy with five existing popular methods using synthetic and real-world datasets. The results show that the proposed method has higher detection accuracy. Results: The detection precision and recall of the proposed method were improved. For the detection accuracy, it is superior to popular methods, indicating that the proposed method has a good detection effect in identifying outlier. Conclusion: Compared with popular methods, the proposed method has a slight advantage in detection accuracy and is one of the effective algorithms that can be selected for outlier detection.

Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients

A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. To address these issues, in this paper, we present an unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD (Interpretable Single dimension Outlier Detection). ISOD first fulfils the empirical cumulative distribution function before computing the quantile and skewness coefficients of each dimension. Finally, it outputs the outlier score. This paper’s contributions are as follows: (1) we propose an unsupervised outlier detection algorithm called ISOD, which has high interpretability and scalability; (2) massive experiments on benchmark datasets demonstrated the superior performance of the ISOD algorithm compared with state-of-the-art baselines in terms of ROC and AP.

Detecting Outliers Using Modified Recursive PCA Algorithm For Dynamic Streaming Data

Outlier analysis has been widely studied and has produced many methods. However, there is still rare a method to detect outliers for dynamically streaming batch data (online learning). In the present research, a novel online algorithm to detect outliers in such dataset is proposed. Data points are proceeded by applying a modified recursive PCA to predict sequentially parameters of the model; eigenvalues and eigenvectors of the statistical detection model are recursively updated using approximate values by perturbation methods. More specifically, the recursive eigenstructure is obtained from the derivation of the covariance matrix using the first-order perturbation technique. The Mahalanobis distance is then used as an outlier score. Our algorithm performances are evaluated using some metrics, namely accuration, precision, recall, F1-score, AUC-PR, and the execution time. Results show that the proposed online outlier detection is computationally efficient in time and the algorithm's performance effectiveness is comparable to that of the offline outlier detection algorithm via classical PCA.

THE EFFECTS OF REIMPLEMENTING A BIOMETRIC ATTENDANCE MONITORING SYSTEM IN THE ELECTRONICS ENGINEERING DEPARTMENT AT DON HONORIO VENTURA STATE UNIVERSITY

Monitoring the attendance of staff and employees holds paramount importance in organizational settings. This can be accomplished either manually, using logbooks and daily time records, or automatically through advanced technologies such as radio frequency identification, biometrics, and bar code scanners. Biometrics attendance monitoring systems, in particular, offer a range of benefits, including heightened security, accuracy, convenience, scalability, time efficiency, compliance, and accountability. This research paper elucidates the advantages of the biometric system, examining its impact on the employees of the Electronics Engineering Department at Don Honorio Ventura State University during the 1st Semester of the Academic Year 2023-2024. The Biometrics Attendance System proves to be exceptionally effective and efficient, gaining a consensus of 92% among participants regarding its positive impacts. Ratings consistently hover at 4 or 5 for efficiency, underscoring the streamlined processes. Accessibility earns a positive average score of 80%, and privacy measures receive an equally commendable average score of 82%. While the system generally contributes positively to managing absenteeism, an outlier score of 1 from a participant prompts further investigation. In conclusion, the Biometrics Attendance System excels in effectiveness, efficiency, and user satisfaction, positioning itself as a secure, accurate, and convenient solution. This becomes especially relevant as the Electronics Engineering Department integrates the biometric system, reaping benefits in terms of security, accuracy, and time efficiency. However, it's noteworthy that the implementation of the biometrics attendance system correlates with an increase in faculty absenteeism. Addressing specific concerns identified in the Manual Attendance System could guide targeted improvements for a more comprehensive and satisfactory attendance monitoring system overall.

A Novel Unsupervised Outlier Detection Algorithm Based on Mutual Information and Reduced Spectral Clustering

Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised methods and unsupervised methods, suffer from the following problems: curse of dimensionality, lack of labeled data, and hyperparameter tuning. To address these issues, we present a novel unsupervised outlier detection algorithm based on mutual information and reduced spectral clustering, called MISC-OD (Mutual Information and reduced Spectral Clustering—Outlier Detection). MISC-OD first constructs a mutual information matrix between features, then, by applying reduced spectral clustering, divides the feature set into subsets, utilizing the LOF (Local Outlier Factor) for outlier detection within each subset and combining the outlier scores found within each subset. Finally, it outputs the outlier score. Our contributions are as follows: (1) we propose a novel outlier detection method called MISC-OD with high interpretability and scalability; (2) numerous experiments on 18 benchmark datasets demonstrate the superior performance of the MISC-OD algorithm compared with eight state-of-the-art baselines in terms of ROC (receiver operating characteristic) and AP (average precision).

CNV-FB: A Feature bagging strategy-based approach to detect copy number variants from NGS data.

Copy number variation (CNV), as a type of genomic structural variation, accounts for a large proportion of structural variation and is related to the pathogenesis and susceptibility to some human diseases, playing an important role in the development and change of human diseases. The development of next-generation sequencing technology (NGS) provides strong support for the design of CNV detection algorithms. Although a large number of methods have been developed to detect CNVs using NGS data, it is still considered a difficult problem to detect CNVs with low purity and coverage. In this paper, a new calculation method CNV-FB is proposed to detect CNVs from NGS data. The core idea of CNV-FB is to randomly sample the read depth values of the genome fragment, and then each sample is individually detected for outliers, and finally combined into a final outlier score. The CNV-FB method was applied to simulation data and real data experiments and compared with the other five methods of the same type. The results show that the CNV-FB method has a better detection effect than other methods. Therefore, the CNV-FB method may be an effective algorithm for detecting genomic mutations.

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution. In a nutshell, ECOD first estimates the underlying distribution of the input data in a nonparametric fashion by computing the empirical cumulative distribution per dimension of the data. ECOD then uses these empirical distributions to estimate tail probabilities per dimension for each data point. Finally, ECOD computes an outlier score of each data point by aggregating estimated tail probabilities across dimensions. Our contributions are as follows: (1) we propose a novel outlier detection method called ECOD, which is both parameter-free and easy to interpret; (2) we perform extensive experiments on 30 benchmark datasets, where we find that ECOD outperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and scalability; and (3) we release an easy-to-use and scalable (with distributed support) Python implementation for accessibility and reproducibility.

Identifying outlier scores and outlier jurors to reduce manipulation in classical music competitions

AbstractManipulations in classical music competitions are a proven problem that has as yet not been eradicated from the competition practice. Several examples are provided. We aim to compare and analyze different methods of reducing manipulation in classical music competitions, focusing on outlier scores and on outlier jurors. First, we investigate the typical approach of correcting or discarding individual jurors' scores that significantly deviate from the mean or median of scores received by a given candidate. We then introduce a new method that involves the exclusion of outlier jurors (EOJ). This approach implies that all scores of jurors with ratings that substantially differ from those of other jurors are removed and not taken into account when determining the ranking of candidates. The properties of both approaches are discussed in hypothetical voting scenarios, where one or more jurors assign scores that deviate markedly from those awarded by other jurors. Finally, we present examples of applying various methods to real-world data from classical music competitions, demonstrating the potential effectiveness and implications of each approach in reducing manipulation within these events. Two examples are taken from the International Karol Szymanowski Music Competition, which took place in September 2023 in Katowice, Poland, where EOJ was adopted for the first time as the official scoring system in four competition categories: piano, violin, vocal, and string quartet.

Fusing multi-scale fuzzy information to detect outliers

Outlier detection aims to find objects that behave differently from the majority of the data. Existing unsupervised approaches often process data with a single scale, which may not capture the multi-scale nature of the data. In this paper, we propose a novel information fusion model based on multi-scale fuzzy granules and an unsupervised outlier detection algorithm with the fuzzy rough set theory. First, a multi-scale information fusion model is formulated based on fuzzy granules. Then we employ fuzzy approximations to define the outlier factor of multi-scale fuzzy granules centered at each data point. Finally, the outlier score is calculated by aggregating the outlier factors of a set of multi-scale fuzzy granules. Experimental results demonstrate that the proposed method is comparable with or better than the leading outlier detection methods. The codes and datasets are publicly available online at https://github.com/ChenBaiyang/MFIOD.

Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment.

An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model.

Weighted subspace anomaly detection in high-dimensional space

Anomaly detection aims at finding anomalies deviating from the normal data patterns. Virtually all anomaly detection methods create a model of the normal patterns before finding anomalies. In high-dimensional scenarios, due to the curse of dimensionality, it is difficult to construct the model of normal patterns in the full dimensional space. Subspace methods assuming that data can be characterized by low-dimensional manifolds have attracted a great deal of research. However, in unsupervised setting, unlabeled data is composed of both the normal and the abnormal data. The existence of anomalies might affect the establishment of the underlying normal subspaces. The undetermined number of the underlying subspaces also brings difficulties in subspace selection. To tackle the aforementioned problems, we come up with a weighted subspace anomaly detection (WSAD) method. We utilize correntropy to construct an objective function to mitigate the influence of the anomalies, which can be regarded as a weighting method for different data. Besides, we introduce an auxiliary variable with block sparsity regularization to achieve adaptive subspace selection, which can be regarded as a weighting method for different subspaces. After the normal underlying subspaces being established, we define the outlier scores by considering the deviation from the underlying subspaces, the local outlier score within subspaces, and the subspace scale. We use the half-quadratic theory to transform the optimization problem defined in WSAD, and apply alternating optimization to solve the transformed problem. Theoretically, we prove the convergence of the optimization algorithm. Experimentally, we demonstrate the effectiveness of the proposed method on both synthetic data and real datasets.

Machine learning for data verification in emissions trading system

Data quality is the cornerstone of any emissions trading system (ETS), although developing an effective assurance mechanism is a considerable challenge. To evaluate potential data quality issues of regulated firms and develop a cost-efficient data verification scheme for the authorities, this study uses domain knowledge and data-driven approaches to identify firms with high data quality risks. Using a unique dataset from China's national ETS, each sample obtains an ensemble outlier score generated by several supervised and unsupervised machine learning techniques, and limited inspection resources are allocated to the facilities with higher scores. Our results show that the models make good predictions where potential misreports are found among the predicted high-risk samples, and 70 % of tampered datapoints are detected in the robust test. The method presented here helps in efficiently verifying firms’ self-report emissions and proposes a feasible solution for intelligent data quality management under ETS context.

Mechanical fault diagnosis of on‐load tap‐changer based on improved variational mode decomposition and relative density‐based outlier score

AbstractIn order to diagnose the mechanical fault of transformer on‐load tap‐changer (OLTC) effectively, a fault diagnosis method based on improved variational mode decomposition (VMD) and relative density‐based outlier score (RDOS) is proposed. Firstly, the signal is decomposed into a series of modal components with narrow band and distinguishing centre frequency by improved VMD based on optimal quality factor. Then, the energy, permutation entropy, power spectrum concentration degree and characteristic frequency of each modal component are calculated, and Laplacian‐score method is used to select the features with better discriminative ability. Finally, a RDOS is used to diagnose the mechanical fault of OLTC, and the mechanical condition is judged according to RDOS value. An experiment is conducted on the OLTC simulation experiment platform and the collected signals are processed. The result shows that the proposed method can diagnose mechanical fault effectively.

A new approach to grant review assessments: score, then rank

BackgroundIn many grant review settings, proposals are selected for funding on the basis of summary statistics of review ratings. Challenges of this approach (including the presence of ties and unclear ordering of funding preference for proposals) could be mitigated if rankings such as top-k preferences or paired comparisons, which are local evaluations that enforce ordering across proposals, were also collected and incorporated in the analysis of review ratings. However, analyzing ratings and rankings simultaneously has not been done until recently. This paper describes a practical method for integrating rankings and scores and demonstrates its usefulness for making funding decisions in real-world applications.MethodsWe first present the application of our existing joint model for rankings and ratings, the Mallows-Binomial, in obtaining an integrated score for each proposal and generating the induced preference ordering. We then apply this methodology to several theoretical “toy” examples of rating and ranking data, designed to demonstrate specific properties of the model. We then describe an innovative protocol for collecting rankings of the top-six proposals as an add-on to the typical peer review scoring procedures and provide a case study using actual peer review data to exemplify the output and how the model can appropriately resolve judges’ evaluations.ResultsFor the theoretical examples, we show how the model can provide a preference order to equally rated proposals by incorporating rankings, to proposals using ratings and only partial rankings (and how they differ from a ratings-only approach) and to proposals where judges provide internally inconsistent ratings/rankings and outlier scoring. Finally, we discuss how, using real world panel data, this method can provide information about funding priority with a level of accuracy in a well-suited format for research funding decisions.ConclusionsA methodology is provided to collect and employ both rating and ranking data in peer review assessments of proposal submission quality, highlighting several advantages over methods relying on ratings alone. This method leverages information to most accurately distill reviewer opinion into a useful output to make an informed funding decision and is general enough to be applied to settings such as in the NIH panel review process.

A Probabilistic Transformation of Distance-Based Outliers

The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Over a variety of tabular and image benchmark datasets, we show that the probabilistic transformation does not impact outlier ranking (ROC AUC) or detection performance (AP, F1), and increases the contrast between normal and outlier score distributions (statistical distance). The experimental findings indicate that it is possible to transform distance-based outlier scores into interpretable probabilities with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and, because existing distance computations are used, it adds no significant computational overhead.

THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS

Histogram is a commonly used tool for visualizing data distribution. It has also been used in semi-supervised and unsupervised anomaly detection tasks. The histogram-based outlier score is a fast unsupervised anomaly detection method that has become more popular because of the rapid increase in the amount of data collected in recent decades. Histogram-based outlier score can be computed using either static or dynamic bin-width histograms. When a histogram contains large gaps, the dynamic bin-width approach is preferred over the static bin-width approach. These gaps in a histogram usually occur as a result of various distributions in real data. When working with a static bin-width histogram, gaps can be utilized to acquire better distinction between outliers and inliers. In this study, we propose an adjusted version of the histogram-based outlier score named adjusted histogram-based outlier score, which considers neighboring bins prior to density estimation. Results from a simulation study and real data application indicate that the adjusted histogram-based outlier score yields a better performance not only in the simulated data but also for various types of real data.

The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies

Given an unsupervised outlier detection task, how should one select i) a detection algorithm, and ii) associated hyperparameter values (jointly called a model)? E ective outlier model selection is essential as di erent algorithms may work well for varying detection tasks, and moreover their performance can be quite sensitive to the values of the hyperparameters (HPs). On the other hand, unsupervised model selection is notoriously difficult, in the absence of hold-out validation data with ground-truth labels. Therefore, the problem is vastly understudied in the outlier mining literature. There exists a body of work that propose internal model evaluation strate- gies for selecting a model. These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models. In this paper, we rst survey internal model evaluation strategies including both those proposed speci cally for outlier detection, as well as those that can be adapted from the unsupervised deep representation learning literature. Then, we investigate their e ectiveness empirically in comparison to simple baselines such as random selection and the popular state-of-the-art detector Isolation Forest (iForest) with default HPs. To this end, we set up (and open-source) a large testbed with 39 detection tasks and 297 candidate models comprised of 8 different detectors and various HP con gurations. We evaluate internal strategies from 7 di erent families on their ability to discriminate between models w.r.t. detection performance, without using any labels. Our study reports a striking nding, that none of the existing and adapted strategies would be practically useful: stand-alone ones are not signi cantly di erent from random, and consensus-based ones do not outperform iForest (w/ default HPs) while being more expensive (as all candidate models need to be trained for evaluation). Our survey stresses the importance of and the standing need for e ective unsupervised outlier model selection, and acts as a call for future work on the problem.