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A comparative study on swarm intelligence for structure learning of bayesian networks

SOFT COMPUTING 2017年第22期21卷 6713-6738页

作者： Ji, Junzhong Yang, Cuicui Liu, Jiming Liu, Jinduo Yin, Baocai Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing Peoples R China Hong Kong Baptist Univ Dept Comp Sci & Technol Kowloon Hong Kong Peoples R China

A bayesian network (BN) is an important probabilistic model in the field of artificial intelligence and a powerful formalism used to describe uncertainty in the real world. As science and technology develop, considerable data on complex systems have been acquired by various means, which presents a significant challenge regarding how to accurately and robustly learn a network structure for a complex system. To address this challenge, many BN structure learning methods based on swarm intelligence have been developed. In this study, we perform a systematic comparison of three typical methods based on ant colony optimization, artificial bee colony algorithm, and bacterial foraging optimization. First, we analyze and summarize their main characteristics from the perspective of stochastic searching. Second, we conduct thorough experimental comparisons to examine the roles of different mechanisms in each method by means of multiaspect metrics, i.e., the K2 score, structural differences, and execution time. Next, we perform further experiments to validate the robustness of different algorithms on some benchmark data sets with noise. Finally, we present the prospects and references for researchers who are engaged in learning BN networks.

关键词： bayesian network structure learning Swarm intelligence Ant colony optimization Artificial bee colony algorithm Bacterial foraging optimization

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Optimising online review inspired product attribute classification using the self-learning particle swarm-based bayesian learning approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第10期57卷 3099-3120页

作者： Maiyar, Lohithaksha M. Cho, SangJe Tiwari, Manoj Kumar Thoben, Klaus-Dieter Kiritsis, Dimitris Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India Ecole Polytech Fed De Lausanne SCI STK DK Mech Engn De Lausanne Switzerland Univ Bremen Fac Prod Engn Bremen Germany

Bowing to the burgeoning needs of online consumers, exploitation of social media content for extrapolating buyer-centric information is gaining increasing attention of researchers and practitioners from service science, data analytics, machine learning and associated domains. The current paper aims to identify the structural relationship between product attributes and subsequently prioritise customer preferences with respect to these attributes while exploiting textual social media data derived from fashion blogs in Germany. A bayesian network structure learning model with the K2score maximisation objective is formulated and solved. A self-tailored metaheuristic approach that combines self-learning particle swarm optimisation (SLPSO) with the K2 algorithm (SLPSOK2) is employed to decipher the highest scored structures. The proposed approach is implemented on small, medium and large size instances consisting of 9 fashion attributes and 18 problem sets. The results obtained by SLPSOK2 are compared with the particle swarm optimisation/K2score, Genetic Algorithm/K2 score and ant colony optimisation/K2 score. Results verify that SLPSOK2 outperforms its hybrid counterparts for the tested cases in terms of computational time and solution quality. Furthermore, the study reveals that psychological satisfaction, historical revival, seasonal information and facts and figure-based reviews are major components of information in fashion blogs that influence the customers.

关键词： bayesian network structure learning self-learning particle swarm optimisation machine learning customer preference ordering fashion products

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Estimating Genome-Wide Gene networks Using Nonparametric bayesian network Models on Massively Parallel Computers

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第3期8卷 683-697页

作者： Tamada, Yoshinori Imoto, Seiya Araki, Hiromitsu Nagasaki, Masao Print, Cristin Charnock-Jones, D. Stephen Miyano, Satoru Univ Tokyo Ctr Human Genome Inst Med Sci Lab DNA Informat AnalMinato Ku Tokyo 1088639 Japan RIKEN Computat Sci Res Program Wako Saitama 3510198 Japan Univ Auckland Dept Mol Med & Pathol Sch Med Sci Fac Med & Hlth Sci Auckland 1 New Zealand Univ Cambridge Rosie Hosp Dept Obstet & Gynaecol Cambridge CB2 0SW England Biomed Res Ctr Natl Inst Hlth Res Cambridge England

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric bayesian networks. Due to the difficulty of learning bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

关键词： Biology and genetics gene networks bayesian network structure learning gene expression data analysis

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AN APPROACH OF WEB SERVICE ORGANIZATION USING bayesian network learning

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JOURNAL OF WEB ENGINEERING 2017年第3-4期16卷 252-276页

作者： Liu, Jianxiao Xia, Zhihua Huazhong Agr Univ Coll Informat Wuhan Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China

How to organize and manage Web services, and help users to select the atomic and a set of services with correlations to meet their functional and non-functional requirements quickly is a key problem to be solved in the era of services computing. Firstly, it uses the three-stage dependency bayesian network structure learning method to organize service clusters which realize different functions. Then it uses the maximum likelihood estimation and bayesian estimation methods to do the parameter learning, and the conditional probability table (CPT) of all the nodes can be got. This method can help users select a set of services with better function in the organized services quickly and accurately. Finally, the effectiveness of the proposed method is validated through experiments and case study.

关键词： Web Service bayesian network structure learning bayesian Estimation Service Organization

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Scoring bayesian networks of mixed variables

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2018年第1期6卷 3-18页

作者： Andrews, Bryan Ramsey, Joseph Cooper, Gregory F. Univ Pittsburgh Pittsburgh PA 15260 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

In this paper we outline two novel scoring methods for learning bayesian networks in the presence of both continuous and discrete variables, that is, mixed variables. While much work has been done in the domain of automated bayesian network learning, few studies have investigated this task in the presence of both continuous and discrete variables while focusing on scalability. Our goal is to provide two novel and scalable scoring functions capable of handling mixed variables. The first method, the Conditional Gaussian (CG) score, provides a highly efficient option. The second method, the Mixed Variable Polynomial (MVP) score, allows for a wider range of modeled relationships, including nonlinearity, but it is slower than CG. Both methods calculate log likelihood and degrees of freedom terms, which are incorporated into a bayesian Information Criterion (BIC) score. Additionally, we introduce a structure prior for efficient learning of large networks and a simplification in scoring the discrete case which performs well empirically. While the core of this work focuses on applications in the search and score paradigm, we also show how the introduced scoring functions may be readily adapted as conditional independence tests for constraint-based bayesian network learning algorithms. Lastly, we describe ways to simulate networks of mixed variable types and evaluate our proposed methods on such simulations.

关键词： bayesian network structure learning Mixed variables Continuous and discrete variables

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bayesian network learning algorithm based on unconstrained optimization and ant colony optimization

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Journal of Systems Engineering and Electronics 2012年第5期23卷 784-790页

作者： Chunfeng Wang Sanyang Liu Mingmin Zhu Department of Mathematical Sciences Xidian University Xi'an 710071 E R. China Department of Mathematics Henan Normal University Xinxiang 453007 E R. China

structure learning of bayesian networks is a wellresearched but computationally hard *** learning bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization（U-ACO-B） to solve the drawbacks of the ant colony optimization（ACO-B）.In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final *** the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B *** experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.

关键词： bayesian network structure learning ant colony optimization unconstrained optimization

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An Efficient and Scalable Algorithm for Local bayesian network structure Discovery

An Efficient and Scalable Algorithm for Local Bayesian Netwo...

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European Conference on Machine learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： de Morais, Sergio Rodrigues Aussem, Alex Univ Lyon F-69000 Lyon France Univ Lyon 1 LIESP Lab F-69622 Villeurbanne France

ISBN: (纸本)9783642159381

We present an efficient and scalable constraint-based algorithm, called Hybrid Parents and Children (HPC), to learn the parents and children of a target variable in a bayesian network. Finding those variables is an important first step in many applications including bayesian network structure learning, dimensionality reduction and feature selection. The algorithm combines ideas from incremental and divide-and-conquer methods in a principled and effective way, while still being sound in the sample limit. Extensive empirical experiments are provided on public synthetic and real-world data sets of various sample sizes. The most noteworthy feature of HPC is its ability to handle large neighborhoods contrary to current CB algorithm proposals. The number of calls to the statistical test, en hence the run-time, is empirically on the order O(n(1.09)), where n is the number of variables, on the five benchmarks that we considered, and O(n(1.21)) on a real drug design characterized by 138,351 features.

关键词： bayesian network structure learning constraint-based methods feature selection

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Generalized bayesian structure learning from Noisy Datasets 24th

Generalized Bayesian Structure Learning from Noisy Datasets

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24th Int Conference on Database Systems for Advanced Applications / 6th Int Workshop on Big Data Management and Service / 4th Int Workshop on Big Data Quality Management / 3rd Int Workshop on Graph Data Management and Analysis

作者： Tang, Yan Chen, Yu Ge, Gaolong Hohai Univ Coll Comp & Informat Nanjing 210098 Jiangsu Peoples R China

ISBN: (纸本)9783030185909;9783030185893

In recent years, with the open data movement around the world, more and more open data sets are available. But, the quality of the datasets poses issues for learning models. This study focuses on learning the bayesian network structure from data sets containing noise. A novel approach called GBNL (Generalized bayesian structure learning) is proposed. GBNL first uses a greedy algorithm to obtain an appropriate sliding window size for any dataset, then it leverages a difference array-based method to quickly improve the data quality by locating the noisy data sections and removing them. GBNL can not only evaluate the quality of the data set but also effectively reduce the noise in the data. We conduct experiments to evaluate GBNL on five large datasets, the experiment results validate the accuracy and the generalizability of this novel approach.

关键词： bayesian network structure learning bayesian score Noise reduction Data quality

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Investigation of fault diagnosis model of rotary kiln based on improved algorithm of bayesian 5

Investigation of fault diagnosis model of rotary kiln based ...

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Fifth International Conference on Instrumentation & Measurement, Computer, Communication, and Control (IMCCC)

作者： Liu, Hao-Ran Lv, Xiao-He Li, Xuan Li, Shi-Zhao Shi, Yong-Hong YanShan Univ Informat Sci & Engn Coll Hebei Prov Key Lab Special Opt Fiber & Opt Fiber Qinhuangdao 066004 Peoples R China YanShan Univ Informat Sci & Engn Coll Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9781467377232

bayesian network is one of the most efficient and reliable method in data mining, and bayesian network structure learning is a key link in the process of bayesian network research. Aiming at the problem of the classic Hill-Climbing algorithm is easy to fall into local optimum and low in efficiency, establishing the Most Weight Supported Tree by calculating the mutual information, and combining the Most Weight Supported Tree and the simplified Hill-Climbing algorithm, proposes a new improved bayesian network structure learning algorithm. Comparing with the classic Hill-Climbing algorithm and K2 algorithm, the simulation experiments shown that the improved algorithm not only can obtain a high accuracy rate model, but improve the efficiency of building model. Based on the improved algorithm and combined with JiDong cement's cement rotary kiln operating data, we can establish the fault diagnosis model of cement rotary kiln and realize a precise and rapid fault diagnosis.

关键词： Most Weight Supported Tree Improved Algorithm bayesian network structure learning Cement Rotary Kiln Fault Diagnosis Model

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Time-Approximation Trade-Offs for learning bayesian networks 12

Time-Approximation Trade-Offs for Learning Bayesian Networks

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12th International Conference on Probabilistic Graphical Models (PGM)

作者： Kundu, Madhumita Parviainen, Pekka Saurabh, Saket Univ Bergen Bergen Norway Univ Bergen Inst Math Sci Bergen Norway

bayesian network structure learning is an NP-hard problem. Furthermore, the problem remains hard even for various subclasses of graphs. Motivated by the hardness of exact learning, we study approximation algorithms for learning bayesian networks. First, we propose a moderately exponential time algorithm with running time O(2(l/kn)) that has an approximation ratio l/k where n is the number of vertices and l and k are user-defined parameters with l <= k. That is, we give time-approximation trade-offs for learning bayesian networks. Second, we present a polynomial time algorithm with an approximation ratio 1/d to find an optimal graph whose connected components have size at most d

关键词： bayesian network structure learning Parameterized Algorithms Approximation Algorithms

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