Outlier Identification Methods Research Articles

An outlier identification method based on parallel clustering and the Adaboost algorithm for power multi-source heterogeneous data

Abstract Influenced by the diversity of data scheduling and the multi-level differentiation of data distribution, the multi-source heterogeneous data of electric power have serious deviations in space and time scheduling, which makes it difficult to identify outliers. Therefore, a method for identifying outliers of multi-source heterogeneous data of electric power based on parallel clustering and the Adaboost algorithm is proposed. A sampling node model of heterogeneous data is constructed, and random numbers that obey Gaussian distribution are generated according to the mean value of data, and their mean square loss values are optimized to form an objective function of abnormal values of multi-source heterogeneous data of electric power. According to parallel clustering and the Adaboost algorithm, a unified format is created to obtain the load control results of abnormal values of multi-source heterogeneous data of electric power, so as to realize the identification of abnormal values of multi-source heterogeneous data of electric power. The experimental results show that at the time point of 18 s, the data identification quality of this method has reached 80, and with the increase of time, its data identification quality remains stable. It is demonstrated that the method has good recognition results.

Anomaly Detection Algorithms for Low-Dimensional and High-Dimensional Data: A Critical Study

Suspicious events or objects that differ from the norm in data can be discovered using anomaly identification. Identifying anomalies is critical for many applicable domains of life, e.g., preventing credit card theft and spotting intrusions into networks. It is possible to detect anomalies on a global scale as well as at the local level. A global outlier is a data point beyond the norm for the entire dataset, while a local outlier may be inside the norm for the entire dataset but outside the surrounding data points. Data outlier identification methods that are performed locally are inadequate. Therefore, better algorithms are required to investigate the high velocity of data and identify local outliers. Machine learning and data mining techniques need to be investigated to determine the pros and cons of anomaly identification residing inside data. The density based LOF method can be applied as the best choice to identify local outliers. While many variants of LOF exist for low-dimensional data, none are suitable for high-dimensional data. This research study discusses LOF, COF, and CBLOF methods for spotting local outliers in low and high-dimensional data. Regarding the size of the dimension, the performance of density-based algorithms is examined based on accuracy and time complexity. In this scenario, CBLOF achieves outstanding results due to its distinctive method of employing cluster-based local outlier detection.

A new unsupervised outlier detection method

Information amount has been shown to be one of the most efficient methods for measuring uncertainty. However, there has been little research on outlier detection using information amount. To fill this void, this paper provides a new unsupervised outlier detection method based on the amount of information. First, the information amount in a given information system is determined, which offers a thorough estimate of the uncertainty of this information system. Then, the relative information amount and the relative cardinality are proposed. Following that, the degree of outlierness and weight function are shown. Furthermore, the information amount-based outlier factor is constructed, which determines whether an object is an outlier by its rank. Finally, a new unsupervised outlier detection method called the information amount-based outlier factor (IAOF) is developed. To validate the effectiveness and advantages of IAOF, it is compared to five existing outlier identification methods. The experimental results on real-world data sets show that this method is capable of addressing the problem of outlier detection in categorical information systems.

IMPRoVED: Integrated Method to Predict PostRouting setup Violations in Early Design Stages

The detail routing process is by far the most time consuming during the physical design flow. Routing starts with an estimation of timing slacks and aims to meet the timing specifications at signoff. In this paper, we propose an improved method to predict the net delays using RandomForestRegressor and thereby predict critical paths early on at the placement stage. Quick timing prediction is also essential in making time-sensitive edits to stepping of the chip based on post-Si feedback. The proposed algorithm is based on five novel features, namely, targeted feature selection, introduction of a one-hot encoding scheme, an outlier identification method, post-route buffer-bloat prediction, and post-route cell sizing prediction. Experimental results on academic benchmarks and industry circuits, both on advanced 10nm process node show that the proposed algorithm has led to significant improvements in accurately predicting timing slacks when compared with state of the art. The proposed algorithm predicts slack within 0.598% of signoff results, whereas the state of art results are erroneous by an average of 53.33% for the same metric. Overall time savings of 44.1% is seen when compared to running the traditional flow, and savings of 90% is seen for obtaining the timing results.

Preconditioning of clinical data for intraocular lens formula constant optimisation using Random Forest Quantile Regression Trees

PurposeTo implement a fully data driven strategy for identifying outliers in clinical datasets used for formula constant optimisation, in order to achieve proper formula predicted refraction after cataract surgery, and to assess the capabilities of this outlier detection method. Methods2 clinical datasets (DS1/DS2: N = 888/403) of eyes treated with a monofocal aspherical intraocular lens (Hoya XY1/Johnson&Johnson Vision Z9003) containing preoperative biometric data, power of the lens implant and postoperative spherical equivalent (SEQ) were transferred to us for formula constant optimisation. Original datasets were used to generate baseline formula constants. A random forest quantile regression algorithm was set up using bootstrap resampling with replacement. Quantile regression trees were grown and the 25% and 75% quantile, and the interquartile range were extracted from SEQ and formula predicted refraction REF for the SRKT, Haigis and Castrop formulae. Fences were defined from the quantiles and data points outside the fences were marked and removed as outliers before recalculating the formula constants. ResultsNB = 1000 bootstrap samples were derived from both datasets, and random forest quantile regression trees were grown to model SEQ versus REF and to estimate the median and 25% and 75% quantiles. The fence boundaries were defined as being from 25% quantile - 1.5·IQR to 75% quantile + 1.5·IQR, with data points outside the fence being marked as outliers. In total, for DS1 and DS2, 25/27/32 and 4/5/4 data points were identified as outliers for the SRKT/Haigis/Castrop formulae respectively. The respective root mean squared formula prediction errors for the three formulae were slightly reduced from: 0.4370 dpt;0.4449 dpt/0.3625 dpt;0.4056 dpt/and 0.3376 dpt;0.3532 dpt to: 0.4271 dpt;0.4348 dpt/0.3528 dpt;0.3952 dpt/0.3277 dpt;0.3432 dpt for DS1;DS2. ConclusionWe were able to prove that with random forest quantile regression trees a fully data driven outlier identification strategy acting in the response space is achievable. In a real life scenario this strategy has to be complemented by an outlier identification method acting in the parameter space for a proper qualification of datasets prior to formula constant optimisation.

Efficient machine learning model to predict fineness, in a vertical raw meal of Morocco cement plant

Soft sensor enables computing parameters that can be physically impossible to measure. This work aims to develop a soft sensor for raw meal fineness in a vertical roller mill of a cement plant. In previous research, some key indicators of the process were ignored. It is well known that vertical raw meal is feeded with heterogenous material that impacts raw meal fineness, and therefore, can deteriorates prediction accuracy of the soft sensor. To deal with this constraint, one-year history of industrial data was exploited and processed by a robust multivariate outlier identification method, followed by process expert outlier verification. Then a particular attention was given to process and material quality parameters using a statistical method to test parameters significance on fineness based on data variance calculation. Eight process and four quality parameters were retained to develop robust nonlinear model using artificial neural network: Bayesian regularization and Levenberg Marquart, then compared with Support vector regression with different acquisition function and kernels to tune and select optimum model hyperparameters. The best model performance was obtained with the Bayesian regularization algorithm.

Assessing the effects of multivariate functional outlier identification and sample robustification on identifying critical PM2.5 air pollution episodes in Medellín, Colombia

Identification of critical episodes of environmental pollution, both as a outlier identification problem and as a classification problem, is a usual application of multivariate functional data analysis. This article addresses the effects of robustifying multivariate functional samples on the identification of critical pollution episodes in Medellín, Colombia. To do so, it compares 18 depth-based outlier identification methods and highlights the best options in terms of precision through simulation. It then applies the two methods with the best performance to robustify a real dataset of air pollution (PM2.5 concentration) in the Metropolitan Area of Medellín, Colombia and compares the effects of robustifying the samples on the accuracy of supervised classification through the multivariate functional DD-classifier. Our results show that 10 out of 20 methods revised perform better in at least one kind outliers. Nevertheless, no clear positive effects of robustification were identified with the real dataset.

Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data

• A data-driven ensemble time-series data estimation model is developed. • Statistical variability and the exogenous factor are considered in the estimation. • The range of normal data is calculated based on the ensemble estimation result. • Contextual and collective outliers in time-series data were effectively identified. In this study, a method to estimate the normal range of groundwater level time-series data was developed to identify outliers in terms of the global, contextual, and collective sense. To evaluate the normal range of groundwater level time-series data, the statistical characteristics of the data and the patterns of the precipitation time-series data were incorporated into the LSTM (Long Short-Term Memory)-based ensemble regressor (i.e., the LER model). Based on the LER model, multiple possible trends of the groundwater level were generated, and the general rules of outlier identification methods (i.e., σ and Tukey’s fences (TF) rules) were applied to the LER ensemble estimation result to finally define the range of the normal data. For outlier identification performance validation, the actual groundwater level acquired from three groundwater monitoring stations in South Korea (i.e., the Pohang–Gibuk (PG), Namwon–Dotong (ND), and Jeju–Sangyae (JS) monitoring wells) and the corresponding precipitation data acquired from the nearest weather stations were applied to the study. As the reference method for comparative performance validation, simple applications of the σ and TF rules were used. For the monitoring data, the developed LER-based outlier identification method evaluates the range of the data that might be explained by the modelled influences of the interest (i.e., normal data range). The developed method showed an outlier identification performance of >70% in general while the performance of the σ and TF rules was mostly <50%. In particular, as the method effectively estimated the seasonal trend and the variability of the groundwater level with consideration of the precipitation patterns and statistics on the groundwater level variation, it is superior for identifying the contextual or collective outliers compared to the simple σ and TF rules. Through in-depth analysis, it can be concluded that the developed LER-based outlier identification method is effective for discriminating the abnormal data by considering the intrinsic statistical characteristics of the original data trend and the exogenous factors. In the aspect of the practical applicability, as the result can be automatically acquired based on real-time monitoring data, the developed method is expected to apply for more efficient maintenance of the monitoring devices by embedding the model as the management software into the monitoring network system.

Benchmarking community/primary care musculoskeletal services: A narrative review and recommendation.

High quality data on service performance is essential in healthcare to evidence efficacy, efficiency, and value. There remains a paucity of publicly reported data in community and primary care musculoskeletal (MSK) services. There is also a lack of guidance on which metrics MSK services should be collecting and reporting, and how this data could be used to directly improve patient outcomes, experiences, and value. A narrative review of the evidence around benchmarking MSK services was undertaken with a focus on how to develop routine data collection within community/primary care settings, and how to develop benchmarking capabilities for the future, looking towards a national MSK audit. This evidence was triangulated with the findings from recent MSK data studies undertaken by the authors and emerging UK policy and guidance in this area. To enable MSK benchmarking services need to collect consistent, standardised outcomes and, therefore, we have developed a recommendation on a minimum MSK 'core outcome set' of Patient Reported Outcome Measures (PROMs) and Patient Reported Experience Measures (PREMs) (PROMs: MSK-HQ, NPRS, WPAI; PREMs: National MSK PREM). In addition, we make recommendations on the use of a standardised evidence-based method for case-mix adjustment and outlier identification (using the following baseline demographics and clinical factors; age, sex, ethnicity, pain site, comorbidities, duration of symptoms, previous surgery, previous pain episodes), alongside considerations on how this data should be integrated and reported within NHS systems. Capturing high quality MSK data in a standardised, consistent, and sustainable way is a significant challenge. Policy holders, commissioners, managers, and clinicians need to be realistic with expectations, and take time to explore barriers to implementation including, funding, digital infrastructure/intra-operability, data sharing/governance, digital literacy, and local/national leadership. Next steps include developing a national MSK audit programme to provide a benchmarking model to support continuous improvements in care quality for patients living with MSK conditions.

Spatiotemporal Distribution and Statistical Analysis of Abnormal Groundwater Level Rising in Poyang Lake Basin

Studies on groundwater have traditionally been based on declining groundwater levels and associated ecological, environmental, and geological problems. However, due to extreme hydrometeorological events and human activities, rising groundwater levels have been observed in many areas. The daily groundwater levels from 2018 to 2020 for the Poyang Lake Basin (PLB) in Jiangxi Province were recorded. The statistical characteristics of abnormal groundwater level rising (AGLR) events and the factors influencing the dynamic changes in groundwater level were analyzed using geostatistical methods and outlier identification methods. The groundwater level in the lower terrain of the PLB has increased significantly in recent years. AGLR events identified by the median absolute deviation and interquartile range methods showed that AGLR events mainly occurred in the spring and summer and were mainly distributed near the surface water bodies. Correlation analysis of the factors influencing the groundwater level revealed that the correlation between precipitation and groundwater level was related to topography. In contrast, the correlation between river stage and groundwater level was related to runoff volume.

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement. In this research, we specially investigated the effectiveness of outlier removal to improve estimation performance of 5 machine learning (ML) methods (Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbor, and Gradient Boosting Machines) for software development effort estimation (SDEE). We propose a novel discretization method based on Golden Section (dubbed as Golden Section based Adaptive Discretization, GSAD) to identify optimal number of outliers for SDEE dataset. The results signify the importance of optimal number of outliers&#x2019; removal to improve estimations. Moreover, the results obtained after applying GSAD technique have been compared with IQR and Cooks&#x2019; distance based outlier identification methods over 4 datasets: ISBSG Release 2021, UCP, NASA93 and China. The empirical results confirm that the performance of ML based SDEE methods is generally improving by employing GSAD and the proposed GSAD method has the ability to compete with the other prevalent outlier identification methods.

Damage Detection of Pile Foundations in Marine Engineering Based on a Multidimensional Dynamic Signature

Zhu, R.; Wang, Q.; Zheng, J.; Su, J., and Wang, N., 2021. Damage detection of pile foundations in marine engineering based on a multidimensional dynamic signature. Journal of Coastal Research, 37(3), 576–588. Coconut Creek (Florida), ISSN 0749-0208.Compared with land engineering, pile foundations in marine engineering have the characteristics of long, free lengths in water and bad service environments. The traditional methods of pile foundation damage detection have considerable limitations in the application in marine engineering. In this paper, a new method for pile foundation damage detection is developed. The method constructs a multidimensional dynamic signature space by fusing multivariate information, uses the Mahalanobis distance to define the difference between the damaged and undamaged units of the structure, and detects damage based on a robust outlier identification method in multidimensional space to realize reliable and automatic damage detection under high noise and complex conditions. The effectiveness and reliability of the method are verified using a finite element model (FEM) of a high-pile wharf under different damage degrees and different intensities of Gaussian white noise. The applicability of the method is then experimentally validated on a physical experimental model of a high-pile wharf. Both the FEM and the experimental results show that the method is capable of detecting pile foundation damage and has strong application potential.

Outlier Detection of Water Quality Data Using Ensemble Empirical Mode Decomposition

Objectives : This study was conducted to propose a new methodology for efficiently identifying and removing various outliers that occur in data collected through automated water quality monitoring systems. In the present study, water temperature data were collected from domestic G_water supply system, and the performance of the proposed methodology was tested for water temperature data collected from domestic G_water supply system.Methods : We applied the following analytical procedure to identify outliers in the water quality data: First, a normality test was performed on the collected data. If normality condition was satisfied, the Z-score was used. However, if the normality condition was not satisfied, outliers were identified using the quartile, and the limitations of the existing methodology were analyzed. Second, we decomposed the intrinsic mode function using empirical mode decomposition and ensemble empirical mode decomposition for the collected data, and then considered the occurrence of modal mixing. Finally, a group of intrinsic mode functions was selected using statistical characteristics to identify outliers. In addition, the performance of the method was verified after removing and interpolating outliers using regression analysis and Cook’s distance.Results and Discussion : In the case of water temperature data, as normality condition was not satisfied, outlier identification was carried out by applying the modified quartile method. It was confirmed that outliers distributed within the seasonal component could not be identified at all. In the case of empirical mode decomposition, modal mixing occurred because of the effect of outliers. However, in the case of the ensemble empirical mode decomposition, modal mixing was resolved and the distinct seasonal components were decomposed as intrinsic mode functions. The intrinsic mode functions were synthesized, which showed statistical correlation with the raw water temperature data. As a result of developing a regression model using the synthesized intrinsic mode functions and raw water temperature data and performing outlier search based on Cook’s distances, we concluded that various outliers distributed within the seasonal component could be effectively identified.Conclusions : Considering that satisfactory results could be derived from statistical analysis of the data collected from the automated water quality monitoring system, it can be concluded that outlier identification procedures are essential. However, in the case of the conventional univariate outlier search method, it is apparent that the outlier search performance is significantly poor for data with strong inherent variability, and the interpolation method for the searched outlier cannot be performed. Conversely, the outlier identification method based on ensemble empirical mode decomposition and regression analysis proposed in this study shows excellent discrimination performance for outliers distributed in data with strong inherent variability. Moreover, this method has the advantage of reducing the analyst’s dependence on subjective judgment by presenting statistical cutoff criteria. An additional advantage of the method is that data can be interpolated after removing outliers using intrinsic mode functions. Therefore, the outlier search and interpolation method proposed in this study is expected to have greater applicability as a more effective analysis tool compared to the existing univariate outlier search method.

Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data.

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough—compared with the rest of the data set that is being analyzed—and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.

Comment on “TVOR: Finding Discrete Total Variation Outliers Among Histograms”

Recent paper “TVOR: Finding Discrete Total Variation Outliers Among Histograms” introduces the Total Variation Outlier Recognizer (TVOR) method for identification of outliers among a given set of histograms. After providing a theoretical discussion of the method and verifying its success on synthetic and population census data, it applies the TVOR model to histograms of ages of Holocaust victims produced using United States Holocaust Memorial Museum data. It purports to identify the list of victims of the Jasenovac concentration camp as potentially suspicious. In this comment paper, we show that the TVOR model and its assumptions are grossly inapplicable to the considered dataset. When applied to the considered data, the model is biased in assigning a higher outlier score to histograms of larger sizes, the set of data points is extremely sparse around the point of interest, the dataset has not been reviewed to remove obvious data processing errors, and, contrary to the model requirements, the distributions of the victims' ages naturally vary significantly across victim lists.

Multiple Outlier Detection Tests for Parametric Models

We propose a simple multiple outlier identification method for parametric location-scale and shape-scale models when the number of possible outliers is not specified. The method is based on a result giving asymptotic properties of extreme z-scores. Robust estimators of model parameters are used defining z-scores. An extensive simulation study was done for comparing of the proposed method with existing methods. For the normal family, the method is compared with the well known Davies-Gather, Rosner’s, Hawking’s and Bolshev’s multiple outlier identification methods. The choice of an upper limit for the number of possible outliers in case of Rosner’s test application is discussed. For other families, the proposed method is compared with a method generalizing Gather-Davies method. In most situations, the new method has the highest outlier identification power in terms of masking and swamping values. We also created R package outliersTests for proposed test.

A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Due to the significant increase of the number of wind-based electricity generation systems, it is important to have accurate information on their operational characteristics, which are typically obtained by processing large amounts of measurements from the individual wind turbines (WTs) and from the whole wind farms (WFs). For further processing of these measurements, it is important to identify and remove bad quality or abnormal data, as otherwise obtained WT and WF models may be biased, or even inaccurate. There are wide ranges of both causes and manifestations of these bad/abnormal data, which are often denoted with the common general term “outlier”. This paper reviews approaches for the detection and treatment of outliers in processing WT and WF measurements, starting from the discussion of the commonly measured parameters, variables and resolutions, as well as the corresponding requirements and recommendations in related standards. Afterwards, characteristics and causes of outliers reported in existing literature are discussed and illustrated, as well as the requirements for the data rejection in related standard. Next, outlier identification methods are reviewed, followed by a review of approaches for testing the success of outlier removal procedures, with a discussion of their potential negative effects and impact on the WT and WF models. Finally, the paper indicates some issues and concerns that could be of interests for the further research on the detection and treatment of outliers in processing WT and WF measurements.

A new method for identification of outliers in immunogenicity assay cut point data

The cut point is an important parameter for immunogenicity assay validation and critical to immunogenicity assessment in clinical trials. FDA (2019) recommends using a statistical approach to derive cut point, with an appropriate outlier removal procedure. In general, the industry follows the methods described in Shankar et al. (2008) and Zhang et al. (2013) among others to determine cut point. Outlier removal is a necessary step during the cut point determination exercise to reduce potential false negative classifications. However, the widely used statistical outlier removal method, namely, Tukey's box-plot method (1.5 times inter-quartile range, IQR), is often found to be overly conservative in the sense that it removes too many “outliers”. Tukey's box-plot method can be used to flag potential outliers for further investigation, however, it is not a hypothesis testing based statistical method. Removing these suspected “outliers” will lead to lower cut point which might confound immunogenicity assessment due to the presence of many low false positives. Besides, the very nature of assay analytical variability has a non-negligible adverse impact on the reliability of ADA classification in terms of false positive and false negative, demanding as large as possible contribution from biological variability relative to analytical variability. A new outlier removal procedure, which takes into account the relative magnitude between biological variability and analytical variability within the sample population, is proposed and statistically justified. After sequential removal of analytical and biological outliers, a 5% false positive rate and 1% false positive rate in screening and confirmatory assays, respectively, are still targeted without increasing potential false negatives. Internal data shows that this practice has minimal impact on assay sensitivity and has the advantage of selecting true positive samples. It is shown that the new procedure is more appropriate for cut point determination.

Robust Locally Weighted Regression for Profile Measurement of Magnesium Alloy Tube in Hot Bending Process

Section flattening often occurs in the hot bending process of magnesium alloy tube with large curvature. In order to control the forming quality of the tube, it is necessary to measure the section profile of the magnesium alloy pipe online. In this paper, the laser vision system is used to measure the profile of magnesium alloy tube. Due to the influence of the environment and the surface quality of the pipe, there are obviously isolated outliers in the profile data, which seriously affects the accuracy and precision of the tube measurement. An outlier identification algorithm based on robust locally weighted regression and PaйTa criterion is proposed. This algorithm is used to identify the typically isolated outliers in the measurement process and discuss its identification ability. Meanwhile, it is compared with the moving mean identifier and the Hampel identifier. Subsequently, the ellipse fitting of profile data was carried out, and the fitting ellipse parameters and fitting precision of the curved section were obtained. At the same time, the fitting results were compared before and after the outliers are eliminated. The experiment proves that the outlier identification method based on robust locally weighted regression and PaйTa criterion can effectively identify outliers in profile data, especially for spot outliers. This algorithm is a robust, accurate, and efficient outlier identification method, which can effectively improve the laser profile measurement accuracy of the pipe section and has great significance for the quality control of magnesium alloy tube.

Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.