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Learning Bayesian Networks That Enable Full Propagation of Evidence

IEEE ACCESS 2020年 8卷 124845-124856页

作者： Constantinou, Anthony C. Queen Mary Univ London QMUL Sch EECS Risk & Informat Management RIM Res Grp Bayesian Artifcial Intelligence Res Lab London E1 4NS England Alan Turing Inst London NW1 2DB England

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disjoint subgraphs that do not enable full propagation of evidence. This problem is highly prevalent in cases where the sample size of the input data is low with respect to the dimensionality of the model, which is often the case when working with real data. The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue. The results show that this constraint helps the algorithm to estimate the number of true edges with higher accuracy compared to the state-of-the-art. Out of the 13 algorithms investigated, the results rank SaiyanH 4(th) in reconstructing the true DAG, with accuracy scores lower by 8.1% (F1), 10.2% (BSF), and 19.5% (SHD) compared to the top ranked algorithm, and higher by 75.5% (F1), 118% (BSF), and 4.3% (SHD) compared to the bottom ranked algorithm. Overall, the results suggest that the proposed algorithm discovers satisfactorily accurate connected DAGs in cases where other algorithms produce multiple disjoint subgraphs that often underfit the true graph.

关键词： Causal discovery conditional independence directed acyclic graphs probabilistic graphical models structure learning

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Multiple-concept feature generative models for multi-label image classification

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COMPUTER VISION AND IMAGE UNDERSTANDING 2015年 136卷 69-78页

作者： Kim, Minyoung Seoul Natl Univ Sci & Technol Dept Elect & IT Media Engn Seoul 139743 South Korea

We consider the problem of multi-label classification where a feature vector may belong to one of more different classes or concepts at the same time. Many existing approaches are devoted for solving the difficult estimation task of uncovering the relationship between features and active concepts, solely from data without taking into account any sensible functional structure. In this paper, we propose a novel probabilistic generative model that aims to describe the core generative process of how multiple active concepts can contribute to feature generation. Within our model, each concept is associated with multiple representative base feature vectors, which shares the central idea of sparse feature modeling with the popular dictionary learning. However, by dealing with the weight coefficients as exclusive latent random variables encoding contribution levels, we effectively frame the coefficient learning task as probabilistic inference. We introduce two parameter learning algorithms for the proposed model: one based on standard maximum likelihood learning via the expectation-maximization algorithm, the other focusing on maximally separating the margin of the true concept configuration away from the class boundary. In the latter we suggest an efficient approximate optimization method where each iteration admits closed-form update with no line search. For several benchmark datasets mostly from the multi-label image classification, we demonstrate that our generative model with proposed estimators can often yield superior prediction performance to existing methods. (C) 2014 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical models Multi-label classification Concept prediction Image classification

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ON THE RELATIVE ASYMPTOTIC EXPRESSIVITY OF INFERENCE FRAMEWORKS

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LOGICAL METHODS IN COMPUTER SCIENCE 2024年第4期20卷 13:1-13:52页

作者： Koponen, Vera Weitkaemper, Felix Uppsala Univ Dept Math Uppsala Sweden Ludwig Maximilians Univ Munchen Inst Informat Munich Germany

. We consider logics with truth values in the unit interval [0, 1]. Such logics are used to define queries and to define probability distributions. In this context the notion of almost sure equivalence of formulas is generalized to the notion of asymptotic equivalence. We prove two new results about the asymptotic equivalence of formulas where each result has a convergence law as a corollary. These results as well as several older results can be formulated as results about the relative asymptotic expressivity of inference frameworks. An inference framework F is a class of pairs (ID, L), where ID = (IDn : n = 1,2,3, . . .), IDn are probability distributions on the set Wn of all a--structures with domain {1, ... , n} (where a- is a first-order signature) and L is a logic with truth values in the unit interval [0, 1]. An inference framework F ' is asymptotically at least as expressive as an inference framework F if for every (ID, L) E F there is (ID ', L ') E F ' such that ID is asymptotically total variation equivalent to ID ' and for every p(x) E L there is p '(x) E L ' such that p '(x) is asymptotically equivalent to p(x) with respect to ID. This relation is a preorder. If, in addition, F is at least as expressive as F ' then we say that F and F ' are asymptotically equally expressive. Our third contribution is to systematize the new results of this paper and several previous results in order to get a preorder on a number of inference systems that are of relevance in the context of machine learning and artificial intelligence.

关键词： probability logic statistical relational artificial intelligence probabilistic graphical models scalable inference finite model theory logical convergence laws logical expressivity

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Recommender system in collaborative learning environment using an influence diagram

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第18期40卷 7193-7202页

作者： Anaya, Antonio R. Luque, Manuel Garcia-Saiz, Tomas UNED Dept Artificial Intelligence Madrid 28040 Spain

Giving useful recommendations to students to improve collaboration in a learning experience requires tracking and analyzing student team interactions, identifying the problems and the target student. Previously, we proposed an approach to track students and assess their collaboration, but it did not perform any decision analysis to choose a recommendation for the student. In this paper, we propose an influence diagram, which includes the observable variables relevant for assessing collaboration, and the variable representing whether the student collaborates or not. We have analyzed the influence diagram with two machine learning techniques: an attribute selector, indicating the most important attributes that the model uses to recommend, and a decision tree algorithm revealing four different scenarios of recommendation. These analyses provide two useful outputs: (a) an automatic recommender, which can warn of problematic circumstances, and (b) a pedagogical support system (decision tree) that provides a visual explanation of the recommendation suggested. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Recommender systems probabilistic graphical models Collaborative learning Data mining Machine learning e-Learning

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Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第12期66卷 7527-7556页

作者： Lee, Loong Kuan Webb, Geoffrey I. Schmidt, Daniel F. Piatkowski, Nico Fraunhofer IAIS Schloss Birlinghoven D-53757 St Augustin Germany Monash Univ Dept Data Sci & AI Melbourne Vic 3800 Australia

The ability to compute the exact divergence between two high-dimensional distributions is useful in many applications, but doing so naively is intractable. Computing the alpha beta-divergence-a family of divergences that includes the Kullback-Leibler divergence and Hellinger distance-between the joint distribution of two decomposable models, i.e., chordal Markov networks, can be done in time exponential in the treewidth of these models. Extending this result, we propose an approach to compute the exact alpha beta-divergence between any marginal or conditional distribution of two decomposable models. In order to do so tractably, we provide a decomposition over the marginal and conditional distributions of decomposable models. We then show how our method can be used to analyze distributional changes by first applying it to the benchmark image dataset QMNIST and a dataset containing observations from various areas at the Roosevelt Nation Forest and their cover type. Finally, based on our framework, we propose a novel way to quantify the error in contemporary superconducting quantum computers.

关键词： probabilistic graphical models Chordal Markov networks Decomposable models Divergences alpha beta-divergence Kullback-Leibler divergence

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Bayesian networks in renewable energy systems: A bibliographical survey

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2016年 62卷 32-45页

作者： Borunda, Monica Jaramillo, O. A. Reyes, Alberto Ibarguengoytia, Pablo H. Consejo Nacl Ciencia & Technol Av Insurgentes Sur 1582 Mexico City 03940 DF Mexico Inst Invest Elect Reforma 113 Col Palmira 62490 Morelos Mexico Univ Nacl Autonoma Mexico Inst Energias Renovables Priv Xochicalco S-N Temixco 62580 Morelos Mexico

For the last years, the research and development in the field of Renewable Energy has been growing due to the need of Renewable Energy as an extended and reliable source of energy. However, the implementation of renewable energy has many complex problems not easily solved with conventional methods. Recently, Artificial Intelligence techniques such as Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms, have been widely used to deal with these problems in the field of Renewable Energy. Nevertheless, issues with a degree of uncertainty need Bayesian Networks since this is one of the most effective theories to face them. This technique can contribute to the Renewable Energy harnessing and other open issues on this field. In this work we show the state of the art of the applications of Bayesian Networks in Renewable Energy, such as solar thermal, photovoltaic, wind, geothermal, hydroelectric energies and biomass. Additionally, we include related topics such as energy storage, smart grids and energy assessment. We classify the literature by areas considering three main subjects: resource evaluation, operation, and applications, and in each section we describe the possible directions to be taken in the research of the field. We find that the main applications are done for forecasting, fault diagnosis, maintenance, operation, planning, sizing and risk management. We conclude that Bayesian Networks are a promising tool for the field of Renewable Energy with potential applications due to their versatility. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Renewable energy Sustainable energy Bayesian networks Dynamic Bayesian networks Artificial intelligence probabilistic graphical models

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UC-LTM: Unidimensional clustering using latent tree models for discrete data

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2018年 92卷 392-409页

作者： Poon, Leonard K. M. Liu, April H. Zhang, Nevin L. Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong Hong Kong Peoples R China Shanghai Univ Finance & Econ Sch Informat Management & Engn Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Hong Kong Peoples R China

This paper is concerned with model-based clustering of discrete data. Latent class models (LCMs) are usually used for this task. An LCM consists of a latent variable and a number of attributes. It makes the overly restrictive assumption that the attributes are conditionally independent given the latent variable. We propose a novel method to relax this assumption. The key idea is to partition the attributes into groups such that correlations among the attributes in each group can be properly modeled by using a single latent variable. The latent variables for the attribute groups are then used to build a number of models, and one of them is chosen to produce the clustering results. The new method produces unidimensional clustering using latent tree models and is named UC-LTM. Extensive empirical studies were conducted to compare UC-LTM with several model-based and distance-based clustering methods. UC-LTM outperforms the alternative methods in most cases, and the differences are often large. Further, analysis on real-world social capital data further shows improved results given by UC-LTM over results given by LCMs in a previous study. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Unidimensional clustering Latent tree models Latent class models probabilistic graphical models Unsupervised learning

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Task-Based Robot Grasp Planning Using probabilistic Inference

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IEEE TRANSACTIONS ON ROBOTICS 2015年第3期31卷 546-561页

作者： Song, Dan Ek, Carl Henrik Huebner, Kai Kragic, Danica KTH Royal Inst Technol S-11428 Stockholm Sweden

Grasping and manipulating everyday objects in a goal-directed manner is an important ability of a service robot. The robot needs to reason about task requirements and ground these in the sensorimotor information. Grasping and interaction with objects are challenging in real-world scenarios, where sensorimotor uncertainty is prevalent. This paper presents a probabilistic framework for the representation and modeling of robot-grasping tasks. The framework consists of Gaussian mixture models for generic data discretization, and discrete Bayesian networks for encoding the probabilistic relations among various task-relevant variables, including object and action features as well as task constraints. We evaluate the framework using a grasp database generated in a simulated environment including a human and two robot hand models. The generative modeling approach allows the prediction of grasping tasks given uncertain sensory data, as well as object and grasp selection in a task-oriented manner. Furthermore, the graphical model framework provides insights into dependencies between variables and features relevant for object grasping.

关键词： Cognitive human-robot interaction grasping learning and adaptive systems probabilistic graphical models recognition

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Using a Bayesian Belief Network to detect healthcare fraud

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Kumaraswamy, Nishamathi Ekin, Tahir Park, Chanhyun Markey, Mia K. Barner, Jamie C. Rascati, Karen Univ Texas Austin Coll Pharm 2409 Univ Ave Stop A1930 Austin TX 78712 USA Texas State Univ Fields Chair Business Analyt Dept Informat Syst & Analyt 601 Univ DrMcCoy 451 San Marcos TX 78666 USA Univ Texas Austin Dept Biomed Engn Computat Biomed Engn 107 W Dean Keeton St Stop C0800 Austin TX 78712 USA

Healthcare fraud detection algorithms are mostly based on applying machine learning methods to payer-claims data transactions to detect fraudulent activities. However, claim transactions are often analyzed in isolation, disregarding the interconnections that naturally exist in the claims generated from a single unit such as the patient, payer, or provider. Fraud detection frameworks have untapped potential to aid downstream processes such as investigations and audits through transparent and interpretable machine learning models. In this study, we show how fraud detection frameworks can benefit from use of a graphical network model called Bayesian Belief Network (BBN) which exploits the relational structure of attributes in a transaction and has better interpretability properties than many competing machine learning methods. BBN model performance is found to be comparable to commonly utilized baseline models such as logistic regression and random forest after validation with a real-life insurance claims dataset. In particular, the F1 score of fraud class for the best performing BBN was 0.15. The proposed BBN model is also considered to be interpretable to auditors and investigators based on common sense qualitative evaluation.

关键词： Medicaid Fraud detection Supervised learning probabilistic graphical models

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Ontology-based conditional random fields for object recognition

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KNOWLEDGE-BASED SYSTEMS 2019年 168卷 100-108页

作者： Ruiz-Sarmiento, Jose-Raul Galindo, Cipriano Monroy, Javier Moreno, Francisco-Angel Gonzalez-Jimenez, Javier Univ Malaga Syst Engn & Automat Dept Machine Percept & Intelligent Robot Grp Campus Teatinos E-29071 Malaga Spain Univ Malaga Biomed Res Inst Malaga IBIMA Campus Teatinos E-29071 Malaga Spain

Object recognition is a cornerstone task in autonomous and/or assistance systems like robots, autonomous vehicles, or those assisting to visually impaired, aiming to achieve a certain level of understanding of their surroundings. probabilistic models, such as Conditional Random Fields (CRFs), have been successfully applied to this end given their ability to exploit contextual and situation information, e.g. a bowl is typically found in a cabinet and not in a night-stand. In this work we propose to evolve CRFs into Ontology based Conditional Random Fields (obCRFs), which define a multi-level structure where each level assigns a category with different granularity to the same set of objects. For example, a level could assign to an object the category appliance or furniture, while the next one could categorize it into the tv, microwave, cabinet, or table types. In this way, general categorizations can guide the classification into more specialized ones (and vice versa), improving recognition success, and mitigating the CRFs limitations when modeling a high number of object categories (shared, in general, by Machine Learning techniques). To set the categories in each level we propose to mimic the hierarchical structure of ontologies, where categories are naturally codified following a subsumption ordering. This leads us to the second advantage of obCRFs : the multi labeling of objects provides a richer understanding of the scene, which can be leveraged for accomplishing high-level tasks (e.g. object search or scheduling). Our approach has been tested with scenes from two state-of-the-art datasets: Robot@Home and Cornell-RGBD, outperforming the results provided by standard CRFs. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Object recognition Conditional random fields Ontologies probabilistic graphical models

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