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Color and Shape efficiency for Outlier detection from automated to user evaluation

Visual Informatics 2022年第2期6卷 25-40页

作者： Loann Giovannangeli Romain Bourqui Romain Giot David Auber Univ.Bordeaux CNRSBordeaux INPINRIALaBRIUMR 5800F-33400 TalenceFrance

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data.In these representations,data are encoded with various visual attributes depending on the needs of the representation itself.To make coherent design choices about visual attributes,the visual search field proposes guidelines based on the human brain’s perception of features.However,information visualization representations frequently need to depict more data than the amount these guidelines have been validated on.Since,the information visualization community has extended these guidelines to a wider parameter space.This paper contributes to this theme by extending visual search theories to an information visualization context.We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors.Stimuli are defined by color and shape features for the purpose of visually encoding categorical data.The experimental protocol is made of a parameters space reduction step(i.e.,sub-sampling)based on a machine learning model,and a user evaluation to validate hypotheses and measure capacity limits.The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier.When redundantly encoded,the display heterogeneity has no effect on the task.When encoded with one attribute,the difficulty depends on that attribute heterogeneity until its capacity limit(7 for color,5 for shape)is reached.Finally,when encoded with two attributes simultaneously,performances drop drastically even with minor heterogeneity.

关键词： Visual search Outlier detection User evaluation Deep learning Automated evaluation

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A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier detection

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Chinese Journal of Electronics 2019年第5期28卷 899-905页

作者： QU Hua ZHANG Yanpeng LIU Wei ZHAO Jihong School of Software Engineering Xi'an Jiaotong University School of Electronics and Information Engineering Xi'an Jiaotong University School of Communication and Information Engineering Xi'an University of Posts and Telecommunication

We propose a robust fuzzy time series forecasting method based on multi-partition approach and Outlier detection for forecasting market prices. The multipartition approach employs a specific partition criterion for each dimension of the time series. We use a Gaussian kernel version fuzzy C-means clustering to construct the fuzzy logic relationships and detect the outliers by calculating the grade of membership. We apply an additional model, which is trained on the set of outliers by Levenberg-Marquardt algorithm, for forecasting the outliers in testing set. The experiment results show that the proposed method improves the robustness and the average forecasting accuracy rate.

关键词： Fuzzy time series Multi-partition approach Outlier detection

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CLOF Based Outlier detection Algorithm of Temperature Data for Ethylene Cracking Furnace

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Journal of Harbin Institute of Technology(New Series) 2023年第4期30卷 50-57页

作者： Yidan Xin Shaolin Hu Wenzhuo Chen He Song School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China Guangdong Provincial Key Lab.of Petrochemical Equipment and Fault Diagnosis School of AutomationGuangdong University of Petrochemical TechnologyMaoming 525000GuangdongChina

The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace,the outliers of temperature data can lead to the false alarm.Conventional Outlier detection algorithms such as the Isolation Forest algorithm and 3-sigma principle cannot detect the outliers accurately.In order to improve the detection accuracy and reduce the computational complexity,an Outlier detection algorithm for flue temperature data based on the CLOF(Clipping Local outlier Factor,CLOF)algorithm is proposed.The algorithm preprocesses the normalized data using the cluster pruning algorithm,and realizes the high accuracy and high efficiency Outlier detection in the outliers candidate set.Using the flue temperature data of an ethylene cracking furnace in a petrochemical plant,the main parameters of the CLOF algorithm are selected according to the experimental results,and the Outlier detection effect of the Isolation Forest algorithm,the 3-sigma principle,the conventional LOF algorithm and the CLOF algorithm are compared and analyzed.The results show that the appropriate clipping coefficient in the CLOF algorithm can significantly improve the detection efficiency and detection accuracy.Compared with the Outlier detection results of the Isolation Forest algorithm and 3-sigma principle,the accuracy of the CLOF detection results is increased,and the amount of data calculation is significantly reduced.

关键词： temperature data Outlier detection ethylene cracker furnace clustering data clipping LOF

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outlier Behavior detection for Indoor Environment Based on t-SNE Clustering

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Computers, Materials & Continua 2021年第9期68卷 3725-3736页

作者： Shinjin Kang Soo Kyun Kim School of Games Hongik UniversitySejong30016Korea Department of Computer Engineering Jeju National UniversityJeju63243Korea

In this study,we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment.We focus on the users’app usage to analyze unusual behavior,especially in indoor spaces.This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased.Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors.The density-based spatial clustering of applications with noise(DBSCAN)algorithm was applied for effective singular movement analysis.To analyze high-level mobile phone usage,the t-distributed stochastic neighbor embedding(t-SNE)algorithm was employed.These two clustering algorithms can effectively detect outlier behaviors in terms of movement and app usage in indoor spaces.The experimental results showed that our system enables effective spatial behavioral analysis at a low cost when applied to logs collected in actual living spaces.Moreover,large volumes of data required for Outlier detection can be easily acquired.The system can automatically detect the unusual behavior of a user in an indoor space.In particular,this study aims to reflect the recent trend of the increasing use of smartphones in indoor spaces to the behavioral analysis.

关键词： Outlier detection trajectory clustering behavior analysis app data smartphone

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Outlier detection of Mixed Data Based on Neighborhood Combinatorial Entropy

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Computers, Materials & Continua 2021年第11期69卷 1765-1781页

作者： Lina Wang Qixiang Zhang Xiling Niu Yongjun Ren Jinyue Xia School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai519080China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New York10504USA

Outlier detection is a key research area in data mining technologies,as Outlier detection can identify data inconsistent within a data set.Outlier detection aims to find an abnormal data size from a large data size and has been applied in many fields including fraud detection,network intrusion detection,disaster prediction,medical diagnosis,public security,and image processing.While Outlier detection has been widely applied in real systems,its effectiveness is challenged by higher dimensions and redundant data attributes,leading to detection errors and complicated calculations.The prevalence of mixed data is a current issue for Outlier detection algorithms.An Outlier detection method of mixed data based on neighborhood combinatorial entropy is studied to improve Outlier detection performance by reducing data dimension using an attribute reduction algorithm.The significance of attributes is determined,and fewer influencing attributes are removed based on neighborhood combinatorial entropy.Outlier detection is conducted using the algorithm of local outlier factor.The proposed Outlier detection method can be applied effectively in numerical and mixed multidimensional data using neighborhood combinatorial entropy.In the experimental part of this paper,we give a comparison on Outlier detection before and after attribute reduction.In a comparative analysis,we give results of the enhanced Outlier detection accuracy by removing the fewer influencing attributes in numerical and mixed multidimensional data.

关键词： Neighborhood combinatorial entropy attribute reduction mixed data Outlier detection

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GMDH-Based Outlier detection Model in Classification Problems

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Journal of Systems Science & Complexity 2020年第5期33卷 1516-1532页

作者： XIE Ling JIA Yanlin XIAO Jin GU Xin HUANG Jing School of Medical Information Engineering Zunyi Medical UniversityZunyi 563006China Business School Sichuan UniversityChengdu 610064China Soft Science Institute Sichuan UniversityChengdu 610064China School of Public Administration Sichuan UniversityChengdu 610064China

In many practical classification problems,datasets would have a portion of outliers,which could greatly affect the performance of the constructed models.In order to address this issue,we apply the group method of data handin neural network in Outlier detection.This study builds a GMDH-based outlier detectio model.This model first implements feature selection in the training set L using GMDH neural network.Then a new training set L can be obtained by mapping the selected key feature subset.Next,a linear regression model can be constructed in the set L by ordinary least squares estimation.Further,it eliminates a sample from the set L randomly every time,and then rebuilds a linear regression model.Finally,Outlier detection is realized by calculating Cook’s distance for each sample.Four different customer classification datasets are used to conduct experiments.Results show that GOD model can effectively eliminate outliers,and compared with the five existing Outlier detection models,it generally performs significantly better.This indicates that eliminating outliers can effectively enhance classification accuracy of the trained classification model.

关键词： Classification problem Cook’s distance feature selection GMDH Outlier detection

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A Two-Level Approach based on Integration of Bagging and Voting for Outlier detection

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Journal of Data and Information Science 2020年第2期5卷 111-135页

作者： Alican Dogan Derya Birant The Graduate School of Natural and Applied Sciences Dokuz Eylul UniversityIzmirTurkey Department of Computer Engineering Dokuz Eylul UniversityIzmirTurkey

Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.Design/methodology/approach:This paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems.The proposed approach,named Bagged and Voted Local Outlier detection(BV-LOF),benefits from the Local outlier Factor(LOF)as the base algorithm and improves its detection rate by using ensemble methods.Findings:Several experiments have been performed on ten benchmark Outlier detection datasets to demonstrate the effectiveness of the BV-LOF method.According to the results,the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average.Research limitations:In the BV-LOF approach,the base algorithm is applied to each subset data multiple times with different neighborhood sizes(k)in each case and with different ensemble sizes(T).In our study,we have chosen k and T value ranges as[1-100];however,these ranges can be changed according to the dataset handled and to the problem addressed.Practical implications:The proposed method can be applied to the datasets from different domains(i.***.health,finance,manufacturing,etc.)without requiring any prior information.Since the BV-LOF method includes two-level ensemble operations,it may lead to more computational time than single-level ensemble methods;however,this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree.Originality/value:The proposed approach(BV-LOF)investigates multiple neighborhood sizes(k),which provides findings of instances with different local densities,and in this way,it provides more likelihood of Outlier detection that LOF may neglect.It also brings many benefits such as easy implementation,improved capability,higher applicability

关键词： Outlier detection Local outlier factor Ensemble learning Bagging Voting

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Outlier detection for Water Supply Data Based on Joint Auto-Encoder

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Computers, Materials & Continua 2020年第7期64卷 541-555页

作者： Shu Fang Lei Huang Yi Wan Weize Sun Jingxin Xu Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics(SZ) College of Electronics and Information EngineeringShenzhen UniversityShenzhen518061China Water Resources Management Center of Ministry of Water Resources BeijingChina Departmet of Housing and Public Works QueenslandAustralia

With the development of science and technology,the status of the water environment has received more and more attention.In this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the problem of Outlier detection in water supply data.The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an output.The outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an outlier.For water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to zero.We set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers respectively.The data samples with reconstruction errors exceeding the thresholds are voted to be outliers.The two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)curve.We have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data set.As a result,our model has proved to outperform the vanilla Auto-Encoder and some other Outlier detection approaches with the recall rate of 98.94 percent in water supply data.

关键词： Water supply data Outlier detection auto-encoder deep learning

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Probabilistic Outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

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Geoscience Frontiers 2021年第1期12卷 425-439页

作者： Shuo Zheng Yu-Xin Zhu Dian-Qing Li Zi-Jun Cao Qin-Xuan Deng Kok-Kwang Phoon State Key Laboratory of Water Resources and Hydropower Engineering Science Institute of Engineering Risk and Disaster PreventionWuhan University299 Bayi RoadWuhan 430072China Department of Civil and Environmental Engineering National University of SingaporeBlk E1A#07-031 Engineering Drive 2Singapore 117576Singapore

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic Outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,Outlier detection)for uncertainty quantification based on geoscience data.

关键词： Outlier detection Site investigation Sparse multivariate data Mahalanobis distance Resampling by half-means Bayesian machine learning

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GA-iForest: An Efficient Isolated Forest Framework Based on Genetic Algorithm for Numerical Data Outlier detection

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Transactions of Nanjing University of Aeronautics and Astronautics 2019年第6期36卷 1026-1038页

作者： LI Kexin LI Jing LIU Shuji LI Zhao BO Jue LIU Biqi College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China State Grid Liaoning Electric Power Supply Co. LTDShenyang 110004P.R.China

With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,Outlier detection is related to the quality of data.The isolated forest algorithm is one of the more prominent numerical data Outlier detection algorithms in recent years.In the process of constructing the isolation tree by the isolated forest algorithm,as the isolation tree is continuously generated,the difference of isolation trees will gradually decrease or even no difference,which will result in the waste of memory and reduced efficiency of Outlier detection.And in the constructed isolation trees,some isolation trees cannot detect outlier.In this paper,an improved iForest-based method GA-iForest is proposed.This method optimizes the isolated forest by selecting some better isolation trees according to the detection accuracy and the difference of isolation trees,thereby reducing some duplicate,similar and poor detection isolation trees and improving the accuracy and stability of Outlier detection.In the experiment,Ubuntu system and Spark platform are used to build the experiment environment.The outlier datasets provided by ODDS are used as test.According to indicators such as the accuracy,recall rate,ROC curves,AUC and execution time,the performance of the proposed method is evaluated.Experimental results show that the proposed method can not only improve the accuracy and stability of Outlier detection,but also reduce the number of isolation trees by 20%-40%compared with the original iForest method.

关键词： Outlier detection isolation tree isolated forest genetic algorithm feature selection

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