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Robust Model Averaging Method Based on lof algorithm

Communications in Mathematical Research 2023年第3期39卷 386-413页

作者： Fan Wang Kang You Guohua Zou School of Mathematical Sciences Capital Normal UniversityBeijing 100048P.R.China

Model averaging is a good alternative to model selection,which can deal with the uncertainty from model selection process and make full use of the information from various candidate ***,most of the existing model averaging criteria do not consider the influence of outliers on the estimation *** purpose of this paper is to develop a robust model averaging approach based on the local outlier factor(lof)algorithm which can downweight the outliers in the *** optimality of the proposed robust model averaging estimator is derived under some regularity ***,we prove the consistency of the lof-based weight estimator tending to the theoretically optimal weight *** studies including Monte Carlo simulations and a real data example are provided to illustrate our proposed methodology.

关键词： Outliers lof algorithm robust model averaging asymptotic optimality consistency

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A Novel Hybrid Model Merging lof and iForest algorithms for Insider Threats Detection 4

A Novel Hybrid Model Merging LOF and iForest Algorithms for ...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Mahajan, Anvita Ajay College of Engineering Pune India

ISBN: (纸本)9798350354218

Insider threats are among the most difficult challenges in cybersecurity, often leading to substantial losses for organizations. Insider threats remain a well-researched area of cybersecurity, with continuous development of detection techniques to combat this persistent challenge. This research investigates the effectiveness of various anomaly detection algorithms, specifically focusing on the Locality Outlier Factor (lof) and Isolation Forest (IF) algorithms. The study conducts a comprehensive comparative analysis to evaluate their performance in identifying anomalous data points. By delving into the strengths and weaknesses of each approach, the research aims to provide valuable insights into choosing the most suitable algorithm for different anomaly detection applications. This paper goes beyond individual anomaly detection algorithms by proposing a novel hybrid model that leverages the strengths of both Locality Outlier Factor (lof) and Isolation Forest (IF). This innovative approach demonstrably outperforms both standalone methods in detecting insider threats, achieving a remarkable accuracy of 99%. © 2024 IEEE.

关键词： CERT Hybrid Model IF algorithm lof algorithm threats

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Predictive control of SCR denitrification system in thermal power plants based on GA-BP and PSO

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第10期101卷 5818-5831页

作者： Tang, Minan An, Bo Yan, Yaguang Zhang, Yaqi Wang, Wenjuan Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China Lanzhou Jiaotong Univ Sch New Energy & Power Engn Lanzhou Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Peoples R China

Aiming at the characteristics of strong non-linearity and large inertia in the reaction process of a selective catalytic reduction (SCR) denitrification system, a predictive control algorithm based on a back propagation neural network optimized by genetic algorithm (GA-BP) and particle swarm optimization (PSO) is proposed. First, the prediction model of a SCR denitrification system is established by GA-BP. Second, output feedback and bias correction are used to reduce the prediction error. Third, the optimal inlet ammonia concentration is obtained by PSO. At the same time, in order to solve the problems of high dimension, large noise, and strong coupling in the original data of the SCR system in the process of establishing the prediction model, the least absolute shrinkage selection operator (LASSO) algorithm and the local outlier factor (lof) detection algorithm are used to screen important variables and samples in the original data set of the SCR system to remove redundant variables and outliers. Finally, the simulation results show that the prediction model has good prediction accuracy and that the proposed predictive control method can achieve accurate control of ammonia injection concentration. This method improves the denitrification efficiency and reduces the NOx emission concentration, which can provide good guidance for on-site production.

关键词： GA-BP network LASSO algorithm lof algorithm predictive control SCR denitrification

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Towards Identifying Multicriteria Outliers: An Outranking Relation-Based Approach

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INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2018年第3期10卷 27-38页

作者： Rouba, Baroudi Nait-Bahloul, Safia Univ Abdelhamid Ibn Badis Fac Sci & Technol Sci & Technol Dept Mostaganem Algeria Univ Oran 1 Ahmed Benbella LITIO Lab Oran Algeria

This article tackles the problem of outlier detection in the multicriteria decision aid (MCDA) field. The authors propose an outlier detection method based on binary outranking relations and Local Outlier Factor (lof) algorithm. The outlier is detected by applying lof algorithm on the distribution of the outranking relations generated by a multicriteria outranking method. The proposed approach is illustrated on an artificial example and evaluated on a real life financial problem, the country risk problem.

关键词： lof algorithm Multicriteria Decision Outlier Detection Outranking Relation

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A Network Intrusion Detection algorithm Based on Outlier Mining 6th

A Network Intrusion Detection Algorithm Based on Outlier Min...

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6th International Conference on Communications, Signal Processing, and Systems (ICCSPS)

作者： Ding, Tianyi Zhang, Min He, Dongjie Elect Engn Inst Hefei 230037 Anhui Peoples R China Harbin Inst Technol Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9789811065712;9789811065705

A spectral clustering and Local Outlier Factor (lof) based outlier mining algorithm, which is aiming to solve network intrusion detection problem, is proposed in this paper. First of all, the structure of similarity matrix method in spectral clustering is used for data preprocessing to find out the smaller similarity objects. During this process, the pruning of the outliers is completed, and a set of candidate outliers is obtained. Then, we calculate the local outlier factor of each data object in this set through lof algorithm. And the final results of detection of outliers are acquired. The experimental results show that the proposed algorithm improves the accuracy of detecting outliers and the effectiveness of network intrusion detection.

关键词： Intrusion detection Outliers Spectral clustering lof algorithm Data mining

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algorithm Optimization of Anomaly Detection Based on Data Mining 10

Algorithm Optimization of Anomaly Detection Based on Data Mi...

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10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Zhang, Lei Chen, Yong Liao, Shaowen Hexi Univ Informat Technol Ctr Zhangye 734000 Gansu Peoples R China Army Artillery Training Base Zhangjiakou 075100 Hebei Peoples R China

ISBN: (纸本)9781538651148

In this paper, firstly two improved algorithm methods are introduced, namely INFlof and COF, which are based on lof, then the motivation of each algorithm, the definition of the algorithm and the specific steps of the algorithm are described respectively. Then through summarizing lof, INFlof and COF it can find out the intrinsic link between them: INFlof can solve the problem of edge misjudgment caused by different density cluster's closing to each other in data set, while COF can solve the problem of outliers, but these kinds of two algorithms are from different steps to solve the outlier factor. Finally, the advantages of the these two algorithms are presented, thus the algorithm of this paper is introduced. Moreover, the definition of the algorithm, as well as the specific steps of the algorithm is respectively introduced, besides it also analyzed the time complexity of algorithm.

关键词： Outliers detection lof algorithm Data mining

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A Method for Identifying Gross Errors in Dam Monitoring Data

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WATER 2024年第7期16卷 978页

作者： Chen, Liqiu Gu, Chongshi Zheng, Sen Wang, Yanbo Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210098 Peoples R China

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (lof) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and lof algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

关键词： dam monitoring data gross errors environmental change FCM algorithm OPTICS algorithm lof algorithm

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Real-Time Early Warning Method of Distribution Transformer Load Considering Meteorological Factor Data

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第13期33卷 2450224-2450224页

作者： Li, Shan Huang, Wei Zhou, Yangjun Lu, Xin Yao, Zhiyang Elect Power Res Inst Guangxi Power Grid Co Ltd Nanning 530023 Peoples R China Guangxi Key Lab Intelligent Control & Maintenance Nanning 530023 Peoples R China Guangxi Power Grid Co Ltd Aircraft Patrol & Live Work Ctr Nanning 530023 Peoples R China

The traditional real-time load warning method for distribution transformers has problems such as low recall rate, low warning accuracy, and long warning time, which may lead to potential equipment failures or overload situations not being detected and dealt with in a timely manner, increasing the safety risk of transformer operation and potentially causing safety issues such as equipment damage, fire, or power outage. Therefore, a real-time early warning method of distribution transformer load considering meteorological factor data is designed. The meteorological factor data are collected by the light sensor, humidity sensor, temperature sensor and rainfall sensor, and the load data collection architecture is built by the load monitor, central master station and maintenance station to realize the load data collection of the distribution transformer. The K-nearest neighbor (KNN) method is used to process the missing values of the data, and the lof algorithm is used to determine the local outliers and eliminate the outliers in the data set to achieve data cleaning. Considering the load loss, hot spot temperature and meteorological factors of the distribution transformer, an early warning model is built, and the cleaned data are input into the model to realize Real-time early warning of the distribution transformer load. The experimental results show that the recall rate of this method varies from 95% to 97%, the accuracy rate of early warning is always above 94%, and the maximum value of early warning time is 0.63s. Having good early warning ability.

关键词： Meteorological factor data distribution transformer load real-time warning K-nearest neighbor method lof algorithm load loss hot spot temperature

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Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking algorithm

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Computer Systems Science & Engineering 2024年第3期48卷 723-738页

作者： Yinkong Wei Mucong Wu Wei Wei Paulo R.F.Rocha Ziyi Cheng Weifang Yao Key Laboratory of Operation and Control of Terraced Hydropower Plants in Hubei Province Three Gorges UniversityYichang443002China School of Electricity and New Energy Three Gorges UniversityYichang443002China Marketing Service Center(Metering Center) State Grid Hubei Electric Power Co.Ltd.Wuhan430072China Centre for Functional Ecology-Science for People&the Planet Associate Laboratory TERRADepartment of Life SciencesUniversity of CoimbraCoimbra3000-456Portugal Research Institute of Electric Power Science State Grid Anhui Electric Power Company LimitedHefei230601China

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic *** ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power ***,the accurate prediction of the ground total electric field remains a technical *** this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field *** show that the Local Outlier Factor(lof)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination ***,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite *** low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.

关键词： DC transmission line total electric field effective data multivariable outliers lof algorithm Stacking algorithm

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