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Classification of 10 m-resolution SPOT data using a combined bayesian network classifier-shape adaptive neighborhood method

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2012年 72卷 36-45页

作者： Yang, Jingxue Wang, Yunpeng Chinese Acad Sci Guangzhou Inst Geochem State Key Lab Organ Geochem Guangzhou 510640 Peoples R China

A hybrid inversion method that combines a bayesian network classifier (BNC) with shape adaptive neighborhoods (SANS) is proposed for the classification of 10-m resolution remote sensing images. BNC uses a directed acyclic graph (DAG) to describe variable relationships. We define feature bands and land use/cover types as the feature and class variables, respectively, and describe them as nodes in the DAG. BNC only uses the posterior probability of the class node, and predicts the class with the highest posterior probability. A SAN, containing spectral, textural, and shape features, is used to study the bayesian network structure, in contrast to methods which only use spectral features. The classification results of the proposed SAN-BNC are compared with those of the spectral-based Maximum Likelihood classifier (MLC), the SAN-based MLC, and the SAN-based Support Vector Machine (SVM) using Guangzhou as a case study. Our results show that BNC and SVM have superior inference abilities relative to MLC. Ten meter resolution images will furnish better classification results using the proposed SAN-BNC procedure. (C) 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： bayesian network classifier Shape adaptive neighborhood Fisher optimal division Maximum Likelihood classifier Remote sensing Classification

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A novel approach to fully representing the diversity in conditional dependencies for learning bayesian network classifier

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INTELLIGENT DATA ANALYSIS 2021年第1期25卷 35-55页

作者： Wang, Limin Chen, Peng Chen, Shenglei Sun, Minghui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Jilin Peoples R China Nanjing Audit Univ Sch Econ Nanjing Jiangsu Peoples R China

bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

关键词： bayesian network classifier maximum correlation criterion minimum dependence criterion conditional independence conditional mutual information

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Efficient heuristics for learning scalable bayesian network classifier from labeled and unlabeled data

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APPLIED INTELLIGENCE 2024年第2期54卷 1957-1979页

作者： Wang, Limin Wang, Junjie Guo, Lu Li, Qilong Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Coll Software Changchun 130012 Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Changchun 130012 Peoples R China

Naive Bayes (NB) is one of the top ten machine learning algorithms whereas its attribute independence assumption rarely holds in practice. A feasible and efficient approach to improving NB is relaxing the assumption by adding augmented edges to the restricted topology of NB. In this paper we prove theoretically that the generalized topology may be a suboptimal solution to model multivariate probability distributions if its fitness to data cannot be measured. Thus we propose to apply log-likelihood function as the scoring function, then introduce an efficient heuristic search strategy to explore high-dependence relationships, and for each iteration the learned topology will be improved to fit data better. The proposed algorithm, called log-likelihood bayesian classifier (LLBC), can respectively learn two submodels from labeled training set and individual unlabeled testing instance, and then make them work jointly for classification in the framework of ensemble learning. Our extensive experimental evaluations on 36 benchmark datasets from the University of California at Irvine (UCI) machine learning repository reveal that, LLBC demonstrates excellent classification performance and provides a competitive approach to learn from labeled and unlabeled data.

关键词： bayesian network classifier Attribute independence assumption Ensemble learning Log-likelihood function Instance learning

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Crowd formation detection using bayesian network classifier

Crowd formation detection using Bayesian network classifier

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International Conference on Information Technology and Industrial Engineering

作者： Wang, Zhongfeng Ding, Hui Zhang, Hui Beijing Municipal Inst Labor Protect Beijing Peoples R China Beijing Acad Sci & Technol Beijing Peoples R China Tsinghua Univ Inst Publ Safety Res Dept Engn Phys Beijing Peoples R China

ISBN: (纸本)9781845648442;9781845648435

Crowd event recognition is a basic task in the field of crowd management. In this paper, we present a novel approach for recognition of crowd formation event. Instead of developing general crowd behavior models, our method takes the uncertainty of crowd motion features into account, using bayesian network classifiers, and measures the dependencies within all the crowd motion features using the concept of conditional mutual information. Through experiments, we demonstrate the usefulness of the proposed approach on real data.

关键词： pattern recognition public safety bayesian network classifier crowd event

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Efficient Learning of General bayesian network classifier by Local and Adaptive Search

Efficient Learning of General Bayesian Network Classifier by...

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IEEE International Conference on Data Science & Advanced Analytics

作者： Minn, Sein Fu, Shunkai Desmarais, Michel C. Huaqiao Univ Coll Comp Sci & Technol Xiamen 351021 Peoples R China Ecole Polytech Montreal Dept Comp Engn Montreal PQ H3C 3A7 Canada

ISBN: (纸本)9781479969913

General bayesian network classifier (GBNC) contains only features necessary for classification, so an ideal structure learning solution is to learn GBNC without having to learn the whole bayesian network (BN). A local search based algorithm called LAS-GBNC is proposed. Given faithfulness assumption, LAS-GBNC relies on the information about each variable's appearance in the so-called d-separator(cut set) to sort candidate CI tests dynamically, performing 'effective' ones with priority. Experimental studies indicate that (1) LAS-GBNC achieves the same quality of networks as PC and IPC-BNC, (2) It is much more efficient than PC due to its local search design, and (3) It is obviously faster than IPC-BNC because of its adaptive search strategy.

关键词： Bayes classifier bayesian network bayesian network classifier Markov blanket constraint learning

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Cost Overrun Risk Assessment and Prediction in Construction Projects: A bayesian network classifier Approach

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BUILDINGS 2022年第10期12卷 1660-1660页

作者： Ashtari, Mohammad Amin Ansari, Ramin Hassannayebi, Erfan Jeong, Jaewook Imam Khomeini Int Univ Dept Civil Engn Qazvin *** Iran Sharif Univ Technol Dept Ind Engn Tehran 111558639 Iran Seoul Natl Univ Sci & Technol Dept Safety Engn 232 Gongneung Ro Seoul 01811 South Korea

Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study compared the BN classifier model's performance accuracy to that of the Naive Bayes (NB) and decision tree (DT) models to determine the effect of considering possible correlations between cost overrun risks on prediction accuracy. Moreover, the most critical risks and their relationships are identified by interpreting the learned BN model. The results indicated that the 18 BN models demonstrated an average prediction accuracy of 78.86%, significantly higher than the NB and DT. The present study identified the most significant risks as an increase in the cost of materials, lack of knowledge and experience among human resources, and inflation.

关键词： cost overrun risk assessment machine learning bayesian network classifier naive Bayes decision tree

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Risk prediction for petroleum exploration based on bayesian network classifier

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 228卷

作者： Ren, Hongjia Guo, Qiulin Cao, Zhenglin Ren, Hongbo Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebei Qinhuangdao 066004 Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China Beijing Jingdong Century Trading Co Ltd Beijing 100083 Peoples R China

Effective prediction of petroleum exploration risks is critical for boosting oil and gas extraction efficiency and increasing economic benefits. To improve the accuracy and efficiency of risk prediction, this paper proposes a scalable k-dependence bayesian network classifier (SKDB) method for solving the risk prediction problem of oil and gas exploration. The effectiveness and efficiency of the SKDB method were illustrated by taking the Jurassic Sangonghe Formation hydrocarbon exploration risk prediction in the Junggar Basin as an example. The model accuracy comparison results showed that SKDB has the highest accuracy compared with the state-of-the-art methods, and the accuracy was improved by 3.55 & SIM;7.63%. The oil and gas probability map prediction results indicated that the SKDB method not only showed high hydrocarbon probability in the reservoir areas, but also had better trend inference ability compared with other methods. Based on the SKDB prediction results, the remaining hydrocarbon favorable areas of the Sangonghe Formation reservoir were visualized and three types of favorable hydrocarbon distribution zones were preferentially selected, namely Type I (highly favorable), Type II (moderately favorable) and Type III (low favorable). The application results can provide a decision basis for the next oil and gas exploration and optimization strategy.

关键词： Oil and gas energy Petroleum exploration bayesian network classifier Risk prediction Decision-making

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Learning semi-lazy bayesian network classifier under the c.i.i.d assumption

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KNOWLEDGE-BASED SYSTEMS 2020年 208卷 106422-106422页

作者： Liu, Yang Wang, Limin Mammadov, Musa Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia

bayesian network classifiers (BNCs) are powerful tools in knowledge representation and inference under conditions of uncertainty. In contrast to eager learning, lazy learning seeks to improve the classification accuracy of BNCs by forming a decision theory that is especially tailored for the testing instance, whereas it has received less attention due to the high computational cost at classification time. This study introduces the conditionally independently and identically distributed (c.i.i.d.) assumption to BNCs by assuming that all instances of the same class are conditionally independent of each other and stem from the same probability distribution. Based on this premise, we propose a novel lazy BNC, semi-lazy bayesian network classifier (SLB), which transforms each unlabeled testing instance to a series of complete instances with discriminative supposed class labels, and then builds class-specific local BNCs for each of them. Our experimental comparison on 25 UCI datasets shows that SLB has modest training time overheads and less classification time overheads. The Friedman and Nemenyi tests show that SLB has significant zero-one loss and bias advantages over some state-of-the-art BNCs, such as selective k-dependence bayesian classifier, k-nearest neighbor, lazy bayesian rule and average n-dependence estimators with lazy subsumption resolution. (C) 2020 Elsevier B.V. All rights reserved.

关键词： bayesian network classifier Eager learning Lazy learning Independently and identically distributed assumption

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A Fast Multi-network K-Dependence bayesian classifier for Continuous Features

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PATTERN RECOGNITION 2024年 150卷

作者： Khodayari-Samghabadi, Imaneh Mohammad-Khanli, Leyli Tanha, Jafar Univ Tabriz Dept Elect & Comp Engn POB 19395-4697 Tabriz Iran

One of the bayesian network classifiers widely used in the classification is K -dependence bayesian (KDB). However, most of the KDB classifiers build a single network on a class variable without considering dependencies between features in each class. Moreover, many KDB classifiers need the discretization process to handle continuous features. This paper aims to propose a fast Multi -network K -Dependence bayesian (MNKDB) classifier for continuous features. According to this aim, we propose a non -parametric approach that efficiently identifies dependencies between continuous features in each class with a low computational cost and without discretizing continuous features. The results indicate that the MNKDB classifier is more accurate than the state-of-the-art KDB classifiers, especially for datasets with more than three classes. The MNKDB classifier not only decreases the classification time but also deals with continuous variables without discretizing them. The results for K=2 show that the MNKDB classifier is 36.5, 31.8, and 14.2 times faster and 4.13%, 5.15%, and 5.48% more accurate than the state-of-the-art FKDB (Flexible KDB), KMM-KDB (Kernel Mixture Model based on KDB), and SKDB (Scalable KDB) classifiers, respectively.

关键词： Kernel density estimation B-spline functions Probability density estimation Continuous features bayesian network classifier

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bayesian network classifiers using ensembles and smoothing

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KNOWLEDGE AND INFORMATION SYSTEMS 2020年第9期62卷 3457-3480页

作者： Zhang, He Petitjean, Francois Buntine, Wray Monash Univ Fac Informat Technol Melbourne Vic Australia

bayesian network classifiers are, functionally, an interesting class of models, because they can be learnt out-of-core, i.e. without needing to hold the whole training data in main memory. The selective K-dependence bayesian network classifier (SKDB) is state of the art in this class of models and has shown to rival random forest (RF) on problems with categorical data. In this paper, we introduce an ensembling technique for SKDB, called ensemble of SKDB (ESKDB). We show that ESKDB significantly outperforms RF on categorical and numerical data, as well as rivalling XGBoost. ESKDB combines three main components: (1) an effective strategy to vary the networks that is built by single classifiers (to make it an ensemble), (2) a stochastic discretization method which allows to both tackle numerical data as well as further increases the variance between different components of our ensemble and (3) a superior smoothing technique to ensure proper calibration of ESKDB's probabilities. We conduct a large set of experiments with 72 datasets to study the properties of ESKDB (through a sensitivity analysis) and show its competitiveness with the state of the art.

关键词： bayesian network classifier Ensemble learning Probability smoothing Hierarchical Dirichlet process Attribute discretization

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