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40
A Bayesian method for the induction of probabilistic networks from data
 MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
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Cited by 1254 (30 self)
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This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
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Cited by 191 (0 self)
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This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
Probabilistic independence networks for hidden Markov probability models
, 1996
"... Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been develop ..."
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Cited by 181 (12 self)
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Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a selfcontained review of the basic principles of PINs. It is shown that the wellknown forwardbackward (FB) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.
Learning Bayesian Networks from Data: An InformationTheory Based Approach
, 2001
"... This paper provides algorithms that use an informationtheoretic analysis to learn Bayesian network structures from data. Based on our threephase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional indepe ..."
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Cited by 112 (4 self)
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This paper provides algorithms that use an informationtheoretic analysis to learn Bayesian network structures from data. Based on our threephase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.
Comparing Bayesian Network Classifiers
, 1999
"... In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers  NaïveBayes, tree augmented NaïveBayes, BN augmented NaïveBayes and general BNs, where the latter two are learned using two variants of a conditionalindependence (CI) based BNlearnin ..."
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Cited by 98 (5 self)
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In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers  NaïveBayes, tree augmented NaïveBayes, BN augmented NaïveBayes and general BNs, where the latter two are learned using two variants of a conditionalindependence (CI) based BNlearning algorithm. Experimental results show the obtained classifiers, learned using the CI based algorithms, are competitive with (or superior to) the best known classifiers, based on both Bayesian networks and other formalisms; and that the computational time for learning and using these classifiers is relatively small. Moreover, these results also suggest a way to learn yet more effective classifiers; we demonstrate empirically that this new algorithm does work as expected. Collectively, these results argue that BN classifiers deserve more attention in machine learning and data mining communities. 1 INTRODUCTION Many tasks  including fault diagnosis, pattern recognition and forecasting  c...
Learning Bayesian Networks by Genetic Algorithms. A case study in the prediction of survival in malignant skin melanoma
, 1997
"... In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem. The methodology is applied to the problem of predicting survival of people after one, three and five years of being ..."
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Cited by 90 (11 self)
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In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem. The methodology is applied to the problem of predicting survival of people after one, three and five years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained model, measured in terms of the percentage of wellclassified subjects, is compared to that obtained by the called NaiveBayes. In both cases, the estimation of the model accuracy is obtained from the 10fold crossvalidation method. 1. Introduction Expert systems, one of the most developed areas in the field of Artificial Intelligence, are computer programs designed to help or replace humans beings in tasks in which the human experience and human knowledge are scarce and unreliable. Although, there are domains in which the tasks can be specifed by logic rules, other domains are characterized by an uncertainty inherent...
Construction of Bayesian Network Structures From Data: A Brief Survey and an Efficient Algorithm
, 1995
"... Previous algorithms for the recovery of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required on ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches: CI tests ..."
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Cited by 80 (8 self)
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Previous algorithms for the recovery of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required on ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches: CI tests are used to generate an ordering on the nodes from the database, which is then used to recover the underlying Bayesian network structure using a nonCltestbased method. Results of the evaluation of the algorithm on a number of databases (e.g., ALARM, LED, and SOYBEAN) are presented. We also discuss some algorithm performance issues and open problems.
Learning Bayesian Network Structures by Searching For the Best Ordering With Genetic Algorithms
 IEEE Transactions on Systems, Man and Cybernetics
, 1996
"... In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con ogy for inducing Bayesian network structures frop3 titute the roblem of the evidence propagation and a database of cases. The methodology is based oap&lll searching for the best ordering of the system vari th ..."
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Cited by 69 (9 self)
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In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con ogy for inducing Bayesian network structures frop3 titute the roblem of the evidence propagation and a database of cases. The methodology is based oap&lll searching for the best ordering of the system vari the problem of the model search. The problem of shies by means of genetic algorithl{. Since his th_vidence propagation consists of once the vMproblem of finding an optimal ordea. teeuarue}rables are known, the assignment of resembles the traveling salesman p'FolUleh)ve use .... IW. ....... probablhles to the values of the rest of the van genetic operators that were developed for the latter  problem. The quality of a variable ordering is eval ables. Cooper [4] demonstrated that this problem Mated with the algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties
, 1996
"... This paper was partially presented at the 9th conference on Uncertainty in Artificial Intelligence, July 1993. ..."
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Cited by 58 (0 self)
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This paper was partially presented at the 9th conference on Uncertainty in Artificial Intelligence, July 1993.
An Algorithm for Bayesian Belief Network Construction from Data
 IN PROCEEDINGS OF AI & STAT’97
, 1997
"... This paper presents an efficient algorithm for constructing Bayesian belief networks from databases. The algorithm takes a database and an attributes ordering (i.e., the causal attributes of an attribute should appear earlier in the order) as input and constructs a belief network structure as output ..."
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Cited by 54 (5 self)
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This paper presents an efficient algorithm for constructing Bayesian belief networks from databases. The algorithm takes a database and an attributes ordering (i.e., the causal attributes of an attribute should appear earlier in the order) as input and constructs a belief network structure as output. The construction process is based on the computation of mutual information of attribute pairs. Given a data set which is large enough and has a DAGIsomorphic probability distribution, this algorithm guarantees that the perfect map [1] of the underlying dependency model is generated, and at the same time, enjoys the time complexity of O N ( ) on conditional independence (CI) tests. To evaluate this algorithm, we present the experimental results on three versions of the wellknown ALARM network database, which has 37 attributes and 10,000 records. The correctness proof and the analysis of computational complexity are also presented. We also discuss the features of our work and relate it to previous works.