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17
The maxmin hillclimbing bayesian network structure learning algorithm
 Machine Learning
, 2006
"... Abstract. We present a new algorithm for Bayesian network structure learning, called MaxMin HillClimbing (MMHC). The algorithm combines ideas from local learning, constraintbased, and searchandscore techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian n ..."
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Cited by 151 (8 self)
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Abstract. We present a new algorithm for Bayesian network structure learning, called MaxMin HillClimbing (MMHC). The algorithm combines ideas from local learning, constraintbased, and searchandscore techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesianscoring greedy hillclimbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and stateoftheart algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at
Bayesian Network Structure Learning by Recursive Autonomy Identification
, 2009
"... We propose the recursive autonomy identification (RAI) algorithm for constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous substru ..."
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Cited by 10 (3 self)
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We propose the recursive autonomy identification (RAI) algorithm for constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous substructures. The sequence of operations is performed recursively for each autonomous substructure while simultaneously increasing the order of the CI test. While other CB algorithms dseparate structures and then direct the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. By this means and due to structure decomposition, learning a structure using RAI requires a smaller number of CI tests of high orders. This reduces the complexity and runtime of the algorithm and increases the accuracy by diminishing the curseofdimensionality. When the RAI algorithm learned structures from databases representing synthetic problems, known networks and natural problems, it demonstrated superiority with respect to computational complexity, runtime, structural correctness and classification accuracy over the
A New Hybrid Method for Bayesian Network Learning With Dependency Constraints
"... Abstract — A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net str ..."
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Cited by 3 (2 self)
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Abstract — A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure. I.
Complex Activity Recognition using Granger Constrained Dynamic Bayesian Network
"... Many scenes in surveillance, sports, and other video domains involve complex multiagent activities where the agents coexist and are interacting in a timevarying manner. For example, in the surveillance domain one person may open a door of a vehicle so another person can load an object before they ..."
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Cited by 3 (0 self)
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Many scenes in surveillance, sports, and other video domains involve complex multiagent activities where the agents coexist and are interacting in a timevarying manner. For example, in the surveillance domain one person may open a door of a vehicle so another person can load an object before they both enter the vehicle. Similarly, team sports involve multiple players acting in a coordinated manner. Our goal is to model and recognize such coordinated activities in video by capturing the most discriminative Granger causal relationships between pairs of time sequences extracted from eventclusters. An activity is represented as a collection of eventclusters that can be instantaneous or occur over a period of time. And, loosely speaking, Granger causality,[1], is an explicit measure of one temporal sequence’s influence on another and is therefore ideal for explicitly capturing the causal relationships between agents. The overall training approach is shown in Figure 1, where the feature data from the activity classes are automatically clustered using a hierarchical divisive clustering algorithm. Activity profiles are then extracted from each eventcluster by
Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets
"... The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid or diagnosis, in particular thanks to its inference capabilities, even when data are incomplete. For classification tasks, Naive Bayes and Augmented Naive Bayes classifiers have shown excellent perform ..."
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The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid or diagnosis, in particular thanks to its inference capabilities, even when data are incomplete. For classification tasks, Naive Bayes and Augmented Naive Bayes classifiers have shown excellent performances. Learning a Naive Bayes classifier from incomplete datasets is not difficult as only parameter learning has to be performed. But there are not many methods to efficiently learn Tree Augmented Naive Bayes classifiers from incomplete datasets. In this paper, we take up the structural em algorithm principle introduced by (Friedman, 1997) to propose an algorithm to answer this question. 1
Analysis of Nasopharyngeal Carcinoma Data with a Novel Bayesian Network Learning Algorithm
"... Abstract — Learning the structure of a bayesian network from a data set is NPhard. In this paper, we discuss a novel heuristic called Polynomial MaxMin Skeleton (PMMS) developped by Tsamardinos et al. in 2005. PMMS was proved by extensive empirical simulations to be an excellent tradeoff between ..."
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Abstract — Learning the structure of a bayesian network from a data set is NPhard. In this paper, we discuss a novel heuristic called Polynomial MaxMin Skeleton (PMMS) developped by Tsamardinos et al. in 2005. PMMS was proved by extensive empirical simulations to be an excellent tradeoff between time and quality of reconstruction compared to all constraint based algorithms, especially for the smaller sample sizes. Unfortunately, there are two main problems with PMMS: it is unable to deal with missing data nor with datasets containing functional dependencies between variables. In this paper, we propose a way to overcome these problems. The new version of PMMS is first applied on standard benchmarks to recover the original structure from data. The algorithm is then applied on the Nasopharyngeal carcinoma (NPC) made up from only 1289 uncomplete records in order to shed some light into the statistical profile of the population under study. I.
BAYESIAN NETWORK STRUCTURAL LEARNING AND INCOMPLETE DATA
"... The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, even when data are incomplete. Besides, estimating the parameters of a fixedstructure Bayesian network is ea ..."
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The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, even when data are incomplete. Besides, estimating the parameters of a fixedstructure Bayesian network is easy. However, very few methods are capable of using incomplete cases as a base to determine the structure of a Bayesian network. In this paper, we take up the structural EM algorithm principle [9, 10] to propose an algorithm which extends the Maximum Weight Spanning Tree algorithm to deal with incomplete data. We also propose to use this extension in order to (1) speed up the structural EM algorithm or (2) in classification tasks extend the Tree Augmented Naive classifier in order to deal with incomplete data. 1.
A novel scalable and correct Markov boundary learning algorithm under faithfulness condition
"... In this paper, we propose a novel constraintbased Markov boundary discovery algorithm, called MBOR, that scales up to hundreds of thousands of variables. Its correctness under faithfulness condition is guaranteed. A thorough empiric evaluation of MBOR’s robustness, efficiency and scalability is pro ..."
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In this paper, we propose a novel constraintbased Markov boundary discovery algorithm, called MBOR, that scales up to hundreds of thousands of variables. Its correctness under faithfulness condition is guaranteed. A thorough empiric evaluation of MBOR’s robustness, efficiency and scalability is provided on synthetic databases involving thousands of variables. Our experimental results show a clear benefit in several situations: large Markov boundaries, weak associations and approximate functional dependencies among the variables. 1
METHOD Open Access
"... We present a novel pipeline and methodology for simultaneously estimating isoform expression and allelic imbalance in diploid organisms using RNAseq data. We achieve this by modeling the expression of haplotypespecific isoforms. If unknown, the two parental isoform sequences can be individually re ..."
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We present a novel pipeline and methodology for simultaneously estimating isoform expression and allelic imbalance in diploid organisms using RNAseq data. We achieve this by modeling the expression of haplotypespecific isoforms. If unknown, the two parental isoform sequences can be individually reconstructed. A new statistical method, MMSEQ, deconvolves the mapping of reads to multiple transcripts (isoforms or haplotypespecific isoforms). Our software can take into account nonuniform read generation and works with pairedend reads. Background Highthroughput sequencing of RNA, known as RNAseq, is a promising new approach to transcriptome profiling. RNAseq has a greater dynamic range than microarrays, which suffer from nonspecific hybridization and saturation biases. Transcriptional subsequences spanning multiple exons can be directly observed, allowing more precise estimation of the expression levels of splice variants. Moreover, unlike traditional expression arrays,
Efficient Bayesian Network Learning Using EM or Pairwise Deletion
"... In previous work, we have seen how to learn a TAN classifier from incomplete dataset using the Expectation Maximisation algorithm (François and Leray, 2006). In this paper, we study differences for Bayesian network structure learning between estimating probabilities using the EM algorithm or using ..."
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In previous work, we have seen how to learn a TAN classifier from incomplete dataset using the Expectation Maximisation algorithm (François and Leray, 2006). In this paper, we study differences for Bayesian network structure learning between estimating probabilities using the EM algorithm or using Pairwise Deletion. We have implemented these two estimation techniques with greedy search learning methods in several spaces: Trees, Directed Acyclic Graphs, Completed Partially Directed Acyclic Graphs or Tree Augmented Naive Bayes structures. An experimental study shows strengths and weaknesses of using the EM algorithm or Pairwise Deletion on classification tasks. 1