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Chinese Intelligent Systems Conference (CISC)

作者： Guo, Hongyang Zhang, Rupei Yong, Jiaqi Jiang, Bin Nanjing Univ Aeronaut & Astronaut Automat Coll Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9783662483862;9783662483848

As a representative heuristic search algorithm of structure learning algorithms for Bayesian network, k2 algorithm is easily trapped in local optimum in the process of Bayesian network structure optimization. This paper proposes a k2 algorithm based on chaotic perturbation. First of all, define the order of the node based on mutual information to construct a Bayesian network structure. Then introduce distribution function skew tent map combined with chaotic search. And ergodicity is used to jump out of local optimum and achieve global optimization. Finally, taking steering shaft control structure of B777 control system as an example, the proposed algorithm presents a good effect in Bayesian network structure learning.

关键词： Bayesian network Chaotic perturbation k2 algorithm Mutual information

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Reconstruction of Gene Regulatory Network Based on Bayesian Network

Reconstruction of Gene Regulatory Network Based on Bayesian ...

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2015 International Conference on Advanced Educational Technology and Information Engineering(AETIE 2015)

作者： B.Yu M.H.Wang L.k.Zhao College of Mathematics and Physics Qingdao University of Science and Technology

Gene regulatory network, which focuses on the complex interactions of genes in 1ife, is an important part in the study of the functiona1 genomics and the frontier of bioinformatics and systems biology research. Bayesian network is powerfu1 tools to genetic networks research. As for the empirical analysis, a subset consisting of 15 genes is chosen from yeast gene expressing data as training data, we first use greedy search(GS) algorithm to get the priori information for the k2 algorithm, and then apply the k2 method to the dataset of gene expression profile to get the topological structure of the Bayesian networks, thus inferring the interaction between genes. We verify the effectiveness of our approach by consulting the corresponding experiment articles, and reduce the computational time in learning network, finally get more stable structures.

关键词： Bayesian network gene regulatory network greedy search algorithm k2 algorithm

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Approaching Multi-dimensional Classification by Using Bayesian Network Chain Classifiers 6

Approaching Multi-dimensional Classification by Using Bayesi...

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhang, Ping Yang, Youlong Zhu, Xiaofeng Xidian Univ Sch Math & Stat Xian Peoples R China

ISBN: (纸本)9781479949557

Multi-dimensional classification assigns an unseen instance to more than one class variable simultaneously. Bayesian chain classifiers have been recently proposed to address the task since they were first proposed in 2011. However, when the simplistic structure of Bayesian network is built to represent the dependency relationships among classes, the predictive performance will degrade somewhat. In this paper, we further study Bayesian chain classifiers in more explicit dependency structures for class variables by using score-based methods, that is, we learn general structures of Bayesian network by using the k2 and Hill-Climbing algorithms to represent the dependency relationships among classes. Meanwhile, we employ a fast clustering-based feature subset selection method for constructing selective Naive Bayesian network classifiers, as base classifiers. Ultimately, we develop the alternative Bayesian chain classifiers. Experimental results show that our models can be able to achieve better or at least comparable performance compared against other state-of-the-art methods, both in term of predictive performance and time complexity.

关键词： Multi-dimensional classification Bayesian chain classifiers k2 algorithm Hill-Climbing algorithm Feature selection

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Applying Bayesian information criterion score in binary code quantum-behaved particle swarm optimization algorithm for learning Bayesian networks

Applying Bayesian information criterion score in binary code...

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2014 3rd International Conference on Electrical and Electronics Engineering(ICEEE 2014)

作者： Yingying Wang Rundan Zhang Fei Li College of Communication & Information Engineering Nanjing University of Posts and Telecommunications

Since it is an NP-hard problem to learn a Bayesian network from data, when the number of data is large, the process of learning Bayesian networks structure is prone to get into premature convergence, then obtain local optimal Bayesian networks under Particle Swarm Optimization algorithm(PSO), a novel approach is proposed. The binary code quantum-behaved particle swarm optimization algorithm(BCQPSO) and Bayesian Information Criterion score(BIC) are applied to obtain an optimal Bayesian network. In order to evaluate the matching degree between Bayesian networks and original network structure, BIC score is proposed. The proposed algorithm–BCQPSO is to seek out Bayesian networks that match sample data sets and have a higher BIC score. Besides, in order to get a more optimal network structure, the process of searching has deleted cycle. The ASIA network is used to test the proposed technique, which has a better performance than k2, Maximum Weight Spanning Tree(MWST), and PSO.

关键词： Bayesian networks k2 algorithm MWST structure learning ASIA network binary code quantum-behaved particle swarm optimization algorithm

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Construction of Customer Classification Model Based on Bayesian Network

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JOURNAL OF COMPUTERS 2013年第5期8卷 1200-1206页

作者： Zhu, Qian Zhang, Yingying Hebei Finance Univ Dept Econ & Business Baoding 071051 Peoples R China

At present, the researches on customer segmentation model based on Bayesian network are few. This paper makes a research on the classification problems based on Bayesian network. First of all, it used literature search and case study to describe the related knowledge and classification principles on Bayesian network. After that, combining with Adventure Works Cycles company's customer data, we made the use of k2 learning algorithm to search the best network structure and got two more reasonable Bayesian network topologies. Thereafter, we calculated the posterior probability and selected the largest one of Bayesian network. Then, we adopted 10 - fold stratified cross-validation method to verify the correctness of classification model, and the results are satisfactory. Finally, this paper finds out Bayesian network classification has greater advantages than other classification methods.

关键词： Bayesian network Customer classification k2 algorithm

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Particle Swarm Optimization based method for Bayesian Network Structure Learning

Particle Swarm Optimization based method for Bayesian Networ...

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International Conference on Modeling, Simulation and Applied Optimization

作者： Saoussen Aouay Salma Jamoussi Yassine Ben Ayed MIRACL Multimedia InfoRmation system and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia Sfax BP 1030 - Tunisia

ISBN: (纸本)9781467358125

Bayesian Networks (BNs) are good tools for representing knowledge and reasoning under conditions of uncertainty. In general, learning Bayesian Network structure from a data-set is considered a NP-hard problem, due to the search space complexity. A novel structure-learning method, based on PSO (Particle Swarm Optimization) and the k2 algorithm, is presented in this paper. To learn the structure of a bayesian network, PSO here is used for searching in the space of orderings. Then the fitness of each ordering is calculated by running the k2 algorithm and returning the score of the network consistent with it. The experimental results demonstrate that our approach produces better performance compared to others BN structure learning algorithms.

关键词： Bayesian Networks Particle Swarm Optimization k2 algorithm Structure Learning

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A missing value imputation method using a Bayesian network with weighted learning

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2012年第12期95卷 1-9页

作者： Miyakoshi, Yoshihiro kato, Shohei Nagoya Inst Technol Nagoya Aichi Japan

With the development of computer networks, it has become easy to have huge databases. Accordingly, it is becoming difficult for users to extract knowledge from such databases. In this paper we focus on data mining, especially classification. In real-world data mining, the missing value problem occurs in cases such as speech containing noise, facial occlusion, and the like. When a test sample has missing values, a classification system cannot handle them. In previous studies, various imputation methods have been developed, with the objective of solving the missing value problem with numerous explanatory variables, even if some explanatory variables were ineffective for imputation. It has been said that the use of many variables degrades learning efficiency, and thus we believe that imputation methods should be developed considering the relations among explanatory variables. It is also effective to consider the relations between the test sample and each of the training samples. Therefore, we have proposed an imputation method using a Bayesian network with weighted learning. Experiments have confirmed that the proposed method imputes missed values with approximate values, and the classification system successfully classified test samples in which missed values were imputed by the proposed method, with better success than some conventional imputation methods. (C) 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(12): 19, 2012;Published online in Wiley Online Library (***). DOI 10.1002/ecj.11449

关键词： classification missing value Bayesian network weighted learning k2 algorithm

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MaSO:A Tool for Aiding the Management of Schedule Overrun

MaSO:A Tool for Aiding the Management of Schedule Overrun

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2nd International Advance Computing Conference

作者： Jeet, kawal Dhir, Renu Mago, Vijay kumar Minhas, Rajinder Singh DAV Coll Dept Comp Sci Jalandhar India NIT Dept Comp Sci & Engn Jalandhar India MLUDAV Coll Dept Comp Sci Phagwara India

ISBN: (纸本)9781424447909

Curbing schedule slippage is a daunting task in software industry. This problem evolves mainly because of poor analysis of risk factors and their management. This paper aims to handle this predicament with the help of Influence Diagram (ID). The three main risk factors that adversely affect the schedule are creeping user requirements, requirement instability and use of unnecessary features in the project. An integration of impacts of these with experts' opinion and with the existing databases (consisting of probability of occurrence of risk factors) helped us to create an ID based system that is capable to model the schedule slippage. This system can be used by the software manager at any stage of software development.

关键词： Influence Diagram Schedule Overrun Software Developmen Schedule Management Risk Management Bayesian Network k2 algorithm

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Modeling and estimation of travel behaviors using bayesian network

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2010年第4期4卷 297-305页

作者： Takamiya, Masatoshi Yamamoto, kosuke Watanabe, Toyohide Nagoya Univ Grad Sch Informat Sci Dept Syst & Social Informat Furo Cho Nagoya Aichi 4648603 Japan

Computer simulation method has been used to measure the effect of new Intelligent Transportation Systems (ITS). Prior works which make use of the method simulated simplified travel demand, thus it is necessary to represent the demand correctly. Most of existing researches for forecasting travel behaviors need survey data of travel activity in target city. The data is called Person Trip (PT) data. Therefore, they are not able to be applied to cities where the survey was not conducted. In this paper, we propose a method for modeling and estimating travel behaviors, using Bayesian network (BN). BN is constructed based on dependency zone and trip characteristics. The zones are characterized by the important facilities for travelers. Our method is able to apply to the cities since the zone characteristics are available without PT data. In addition, the dependency is represented as graph structure obtained by using k2 algorithm. Our experimental results show the effectiveness of our method for estimating the behaviors.

关键词： Travel behaviors bayesian network k2 algorithm

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The Multimode Estimation Modeling for Flight Delay of a Busy Hub-Airport in Flight Chain

The Multimode Estimation Modeling for Flight Delay of a Busy...

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IITA International Conference on Services Science, Management and Engineering

作者： Liu, Yujie Ma, Song Tianjin Univ Coll Comp Sci & Technol Tianjin 300072 Peoples R China Civil Aviat Univ China Coll Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9780769537290

Flight delay is the root of Delay Propagation, while delay propagated within a Flight Chain. Busy hub-airport plays an important role in a Flight Chain, since the Initial Delay often happens there. To research more possible impacting factors in the Flight Chain, a busy hub-airport is chosen as the object in this research. After analyzing the complex and uncertain relationship between flight delays and all possible impacting factors, Bayesian network (BN) adapt to model and estimate about flight delay in a busy hub-airport. We proposed two learning methods with three different models. Model I is an estimation model for Arrival Delay based on parameter learning of BN with the Expectation-Maximization (EM) algorithm. Model II is estimation model for Arrival Delay based on structure learning of BN with k2 algorithm. The model learned by k2 is proved more suitable for modeling the estimation of flight delay, with a higher estimating correct rate than Model I. Then ModelIII is estimation model for Departure Delay with the same algorithm of Model II.

关键词： Multimode Modeling Flight Chain Hub-airport Flight Delay Delay Estimation k2 algorithm Baysian Network

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