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An efficient Bayesian network for differential diagnosis using experts’knowledge

International Journal of Intelligent Computing and Cybernetics 2020年第1期13卷 103-126页

作者： Mohammad Mahdi Ershadi Abbas Seifi Department of Industrial Engineering and Management Systems Amirkabir University of TechnologyTehranIran

Purpose-This study aims to differential diagnosis of some diseases using classification methods to support effective medical *** this purpose,different classification methods based on data,experts’knowledge and both are considered in some ***,feature reduction and some clustering methods are used to improve their ***/methodology/approach-First,the performances of classification methods are evaluated for differential diagnosis of different ***,experts’knowledge is utilized to modify the Bayesian networks’*** of the results show that using experts’knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification.A total of ten different diseases are used for testing,taken from the Machine Learning Repository datasets of the University of California at Irvine(UCI).Findings-The proposed method improves both the computation time and accuracy of the classification methods used in this *** networks based on experts’knowledge achieve a maximum average accuracy of 87 percent,with a minimum standard deviation average of 0.04 over the sample datasets among all classification *** implications-The proposed methodology can be applied to perform disease differential diagnosis ***/value-This study presents the usefulness of experts’knowledge in the diagnosis while proposing an adopted improvement method for ***,the Bayesian network based on experts’knowledge is useful for different diseases neglected by previous papers.

关键词： Bayesian network k2 algorithm Experts’knowledge Classification methods Disease and cancer diagnosis

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A Bayesian network model for fault diagnosis of a lock mechanism based on degradation data

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ENGINEERING FAILURE ANALYSIS 2021年 122卷 105225-105225页

作者： Pang, Tianyang Yu, Tianxiang Song, Bifeng Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

This paper aims to build a diagnosis model of a lock mechanism system considering multiple joints wear. The lock mechanism is a complex mechanical system. Diagnosis is difficult because fault modes are not easy to identify. The diagnosis result is affected by the strong dependence of wear between each component. Besides, some lock mechanism?s wear detection data is challenging to acquire because of fewer sensors in some particular situations. For these problems, an improved Bayesian network-based fault diagnosis methodology considering component degradation is proposed to distinguish the fault types. An experiment of congeneric lock mechanisms is conducted, and wear data of the joints is obtained. The Bayesian networks model in which the dependence of components is not considered is established based on experimental data. Because the Bayesian network structure is affected by the strong dependence between components, the k2 algorithm is used to build the Bayesian network structure based on acquired wear data to obtain causality between components. The entire fault model is built by combining two established Bayesian networks. Three fault diagnosis cases are used to validate the accuracy and efficiency of the proposed model. A comparison is made between the improved diagnosis model and the model without considering dependence. Finally, the revolute joints are ranked by the established diagnostic model so that the weakest component can be identified.

关键词： Lock mechanism Bayesian network Fault diagnosis Wear k2 algorithm

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Research on the Influencing Factors of Residents' Travel Based on Bayesian Network

Research on the Influencing Factors of Residents' Travel Bas...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Wang, Fengying Du, Liming Li, Gui Dong, Jie Shenyang Jianzhu Univ 9 Hunnan East Rd Shenyang Liaoning Peoples R China

ISBN: (纸本)9783030152352;9783030152345

Traffic has become an important factor affecting the development of a city and the choice of mode of transport is an important and indispensable component of traffic planning and policy development. In this paper, the urban residents of Shenyang City as the object of study, the application of Bayesian network method, the use of Shenyang city residents travel mode survey data to calculate the influencing factors of the relevant factors and behavioral economics theory of urban residents Travel mode selection to do a whole analysis and research. On the basis of reading a lot of literature, this paper briefly introduces the influencing factors of urban residents 'travel mode, and makes a detailed analysis of residents' travel behavior by using the results of previous literature research. The contents of the analysis mainly include the relative deterministic factors and the relative uncertainty factors that affect the residents' travel mode. Then, the related knowledge and learning methods and parameter estimation of the Bayesian network are introduced. The Bayesian network method and the economic theory Combined to further analyze and study the influencing factors of residents' travel mode. Draw conclusions and provide some suggestions and reference for the improvement of traffic policy.

关键词： Influencing factors Bayesian network k2 algorithm

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Intrusion Detection based on Graph oriented Big Data Analytics

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Procedia Computer Science 2020年 176卷 572-581页

作者： Ahlem Abid Farah Jemili Universite de Sousse ISITCom 4011 Hammam Sousse Tunisia Universite de Sousse ISITCom MARS Research Laboratory LR17ES05 4011 Hammam Sousse Tunisia

Intrusion detection has been the subject of numerous studies in industry and academia. Still, Cybersecurity analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve the intrusion detection system, the visualization of the security events in the form of graphs and diagrams is significant to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and, using a machine learning graph algorithm which can detect in real-time different attacks as early as possible. We use the MAWILab intrusion detection dataset. We choose Microsoft Azure as a unified cloud environment to load our dataset on Azure blob storage. We implement the k2 algorithm, which is a graphical machine learning algorithm to classify attacks. Our system showed a great performance due to the graphical machine learning algorithm and Apache Spark structured streaming engine.

关键词： Intrusion detection MAWILab Apache Spark Structured Streaming Big Data Microsoft Azure Cloud Graph Machine Learning k2 algorithm

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A New Approach for Bayesian Classifier Learning Structure via k2 algorithm

A New Approach for Bayesian Classifier Learning Structure vi...

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8th International Conference on Intelligent Computing (ICIC)

作者： Bouhamed, Heni Masmoudi, Afif Lecroq, Thierry Rebai, Ahmed Univ Rouen LITIS EA 4108 1 Rue Thomas Becket F-76821 Mont St Aignan France Fac Sci Sfax Dept Math Sfax Tunisia Ctr Biotechnol Sfax Bioinformat Unit Sfax Tunisia

ISBN: (纸本)9783642318368

It is a well-known fact that the Bayesian Networks' (BNs) use as classifiers in different fields of application has recently witnessed a noticeable growth. Yet, the Naive Bayes' application, and even the augmented Naive Bayes', to classifier-structure learning, has been vulnerable to certain limits, which explains the practitioners' resort to other more sophisticated types of algorithms. Consequently, the use of such algorithms has paved the way for raising the problem of super-exponential increase in computational complexity of the Bayesian classifier learning structure, with the increasing number of descriptive variables. In this context, the present work's major objective lies in setting up a further solution whereby a remedy can be conceived for the intricate algorithmic complexity imposed during the learning of Bayesian classifiers' structure with the use of sophisticated algorithms. Noteworthy, the present paper's framework is organized as follows. We start, in the first place, by to propose a novel approach designed to reduce the algorithmic complexity without engendering any loss of information when learning the structure of a Bayesian classifier. We, then, go on to test our approach on a car diagnosis and a Lymphography diagnosis databases. Ultimately, an exposition of our conducted work's interests will be a closing step to this work.

关键词： Bayesian Classifier structure learning classification clustering modeling algorithmic complexity k2 algorithm

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Vehicle Lane Change Model Based on Improved Bayesian Network Structure Learning

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International Journal of Intelligent Technologies and Applied Statistics 2018年第4期11卷 255-270页

作者： Tong Dang Fang Dai

In order to reduce traffic safety issues caused by vehicle lane change, a lane change model based on improved Bayesian network structure learning is established. The assumption of the traditional k2 algorithm that the prior probability of the network structure follows the uniform distribution is too arbitrary. Based on the Cooper-Herskovits (CH) scoring function, a new I-CH scoring function, which is constructed by using the connection probability information, is proposed to describe the network structure prior. After selecting experiments samples from the dataset Next Generation Simulation (NGSIM), the samples are discretized by the ChiMerge algorithm and then, the connection probability matrix with initial Bayesian network is obtained by computing the mutual information (MI) between variables. For the problem of node sequence in k2 algorithm, the principal component analysis is applied to optimize the node order and the combining I-CH score was proposed to train and verify this model. The experimental results show that the recognition rate of this model for the lane change behavior is up to 94.9% and the lane change model can be used in real-time situation.

关键词： Traffic safety Vehicle lane change model Bayesian network k2 algorithm I-CH scoring function

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A Bayesian Network for Symptom-diagnosis Data

A Bayesian Network for Symptom-diagnosis Data

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2018 International Conference on Computational, Modeling, Simulation and Mathematical Statistics(CMSMS 2018)

作者： Ying YU Jian-ye LU Zheng YANG Wen-wen YANG Institute of Electromechanical Engineering and Automation Shanghai University

Bayesian network is an important expression tool of uncertain knowledge by integrating probability table and graph. This paper applies Bayesian network into clinical data for knowledge discovery and constructs a Bayesian network for symptom-diagnosis data with optimal structure. First of all, a Markov chain-Monte Carlo based Metropolis-Hastings sampling method is introduced to fill in the missing data;Then, a k2 algorithm is used to search for all possible Bayesian networks among the relationship between symptoms and diagnoses;Further, the BDE scoring function is used to determine the optimal network structure illustrating the relationship between symptoms and diagnoses. The results show that the network structure accuracy has greatly improved after filling the missing data with the method of Metropolis-Hastings sampling.

关键词： Bayesian Markov chain-Monte Carlo Metropolis-Hastings sampling k2 algorithm BDE scoring function

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Learning Bayesian Networks Structure Based Part Mutual Information for Reconstructing Gene Regulatory Networks 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Meng, Qingfei Chen, Yuehui Wang, Dong Meng, Qingfang Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

As a kind of high-precision correlation measurement method, Part Mutual Information (PMI) was firstly introduced into Bayesian Networks (BNs) structure learning algorithm in the paper. Compared to the general search scoring algorithm which set the initial network as an empty network without edge, our training algorithm initialized the network structure as an undirected network. That meant that our initial network identified the genes related to each other. And then the following algorithm only needed to determine the direction of the edges in the network. In the paper, we quoted the classic k2 algorithm based on Bayesian Dirichlet Equivalence (BDE) scoring function to search the direction of the edges. To test the proposed method, We carried out our experiment on two networks: the simulated gene regulatory network and the SOS DNA Repair network of Ecoli bacterium. And via comparison of different methods for SOS DNA Repair network, our proposed method was proved to be effective.

关键词： Gene regulatory networks Bayesian Networks Part Mutual Information k2 algorithm BDE scoring function

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Fault Detection and Diagnosis using Bayesian-Network Inference 43

Fault Detection and Diagnosis using Bayesian-Network Inferen...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Zhang, Yuping You, Liyu Jia, Chunhua Univ Elect Sci & Technol China Sch Automat Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538611272

This paper aims to develop a novel fault detection and diagnosis (FDD) method based on Bayesian network (BN). Firstly, the structure of BN is constructed according to the fault feature, which can be obtained from k2 algorithm and expertise. Then, corresponding parameters of BN are derived from dataset and expertise. Secondly, junction tree algorithm reasoning is provided. Finally, a framework of BN for vehicle start system fault diagnosis is proposed. There are three cases to verify the method. The maximum probability fault can be found according to the way of BN reference results, which demonstrates the feasibility of the developed method.

关键词： Bayesian network fault detection and diagnosis k2 algorithm junction tree reference

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Expectation maximization based ordering aggregation for improving the k2 structure learning algorithm

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INTELLIGENT DATA ANALYSIS 2015年第5期19卷 1003-1018页

作者： Amirkhani, Hossein Rahmati, Mohammad Amirkabir Univ Technol Comp Engn & Informat Technol Dept Tehran Iran

Some of the basic algorithms for learning the structure of Bayesian networks, such as the well-known k2 algorithm, require a prior ordering over the nodes as part of the input. It is well known that the accuracy of the k2 algorithm is highly sensitive to the initial ordering. In this paper, we introduce the aggregation of ordering information provided by multiple experts to obtain a more robust node ordering. In order to reduce the effect of novice participants, the accuracy of each person is used in the aggregation process. The accuracies of participants, not known in advance, are estimated by the expectation maximization algorithm. Any possible contradictions occurred in the resulting aggregation are resolved by modelling the result as a directed graph and avoiding the cycles in this graph. Finally, the topological order of this graph is used as the initial ordering in the k2 algorithm. The experimental results demonstrate the effectiveness of the proposed method in improving the structure learning process.

关键词： Bayesian network structure learning k2 algorithm node ordering knowledge aggregation expectation maximization approach

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