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15
Optimization by learning and simulation of Bayesian and Gaussian networks
, 1999
"... Estimation of Distribution Algorithms (EDA) constitute an example of stochastics heuristics based on populations of individuals every of which encode the possible solutions to the optimization problem. These populations of individuals evolve in succesive generations as the search progresses  organ ..."
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Cited by 43 (6 self)
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Estimation of Distribution Algorithms (EDA) constitute an example of stochastics heuristics based on populations of individuals every of which encode the possible solutions to the optimization problem. These populations of individuals evolve in succesive generations as the search progresses  organized in the same way as most evolutionary computation heuristics. In opposition to most evolutionary computation paradigms which consider the crossing and mutation operators as essential tools to generate new populations, EDA replaces those operators by the estimation and simulation of the joint probability distribution of the selected individuals. In this work, after making a review of the different approaches based on EDA for problems of combinatorial optimization as well as for problems of optimization in continuous domains, we propose new approaches based on the theory of probabilistic graphical models to solve problems in both domains. More precisely, we propose to adapt algorit...
A new approach for learning belief networks using independence criteria
 International Journal of Approximate Reasoning
, 2000
"... q ..."
Learning Causal Networks from Data: A survey and a new algorithm for recovering possibilistic causal networks
, 1997
"... Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For example, diagnosis is usually defined as the task of finding the causes (illnesses) from the observed effects (symptoms). Similarly, prediction can be understood as the description of a future plausible ..."
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Cited by 19 (5 self)
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Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For example, diagnosis is usually defined as the task of finding the causes (illnesses) from the observed effects (symptoms). Similarly, prediction can be understood as the description of a future plausible situation where observed effects will be in accordance with the known causal structure of the phenomenon being studied. Causal models are a summary of the knowledge about a phenomenon expressed in terms of causation. Many areas of the ap # This work has been partially supported by the Spanish Comission Interministerial de Ciencia y Tecnologia Project CICYTTIC96 0878. plied sciences (econometry, biomedics, engineering, etc.) have used such a term to refer to models that yield explanations, allow for prediction and facilitate planning and decision making. Causal reasoning can be viewed as inference guided by a causation theory. That kind of inference can be further specialised into induc
Independency relationships and learning algorithms for singly connected networks
, 1998
"... Graphical structures such as Bayesian networks or M arkov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to efficiently perform reasoning tasks. Singly connected networks are important specific cases where there is no more than one un ..."
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Cited by 18 (10 self)
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Graphical structures such as Bayesian networks or M arkov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to efficiently perform reasoning tasks. Singly connected networks are important specific cases where there is no more than one undirected path connecting each pair of variables. The aim of this paper is to investigate the kind of properties that a dependency model must verify in order to be equivalent to a singly connected graph structure, as a way of driving automated discovery and construction of singly connected networks in data. The main results are the characterizations of those dependency models which are isomorphic to singly connected graphs (either via the dseparation criterion for directed acyclic graphs or via the separation criterion for undirected graphs), as well as the development of efficient algorithms for learning singly connected graph representations of dependency models.
Searching for Bayesian Network Structures in the Space of Restricted Acyclic Aprtially Directed Graphs
 Journal of Artificial Intelligence Research
, 2003
"... Although many algorithms have been designed to construct Bayesian network structures using dierent approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). ..."
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Cited by 15 (2 self)
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Although many algorithms have been designed to construct Bayesian network structures using dierent approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for de ning the elementary modi cations (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a dierent search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of dierent con gurations of the search space is reduced, thus improving eciency. Moreover, although the nal result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to nd better local optima than those obtained by searching in the DAG space.
An effective structure learning method for constructing gene networks
 Bioinformatics
, 2006
"... Motivation: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard ..."
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Cited by 15 (1 self)
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Motivation: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this paper is to present a novel structure learning method for gene network discovery.. Results: In this paper, we present a novel structure learning method to reconstruct the underlying gene networks from the observational gene expression data. Unlike hill climbing approaches, the proposed method first constructs an undirected network based on mutual information between two nodes and then split the structure into substructures. The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure. Our method is evaluated using two benchmark network datasets with known structures. The results show that the proposed method can identify networks that are close to the optimal structures. It outperforms hill climbing methods in terms of both computation time and predicted structure accuracy. We also apply the method to gene expression data measured during the yeast cycle and show the effectiveness of the proposed method for network reconstruction.
Supporting Complex Inquiries
 International Journal of Intelligent Systems
, 1995
"... INTRODUCTION Much of the detectives' and magistrates' task can be regarded as "knowledge processing." They have, typically, to acquire knowledge chunks, find the contradictions inside and across the various depositions, link the consistent hypotheses in a causally connected lattice and judge the cre ..."
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Cited by 12 (9 self)
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INTRODUCTION Much of the detectives' and magistrates' task can be regarded as "knowledge processing." They have, typically, to acquire knowledge chunks, find the contradictions inside and across the various depositions, link the consistent hypotheses in a causally connected lattice and judge the credibility of the information and the reliability of the witnesses. * Sometimes, the complexity of the case could justify the assistance of an appropriate Decision Support System (DSS); we call Inquiry Support System (ISS) this specialized DSS. A first task of an intelligent ISS could be that of generating stereotypical hypotheses about the case under consideration. However, the ultimate task of an ISS should be that of providing a most credible and coherent set of beliefs about the case. Part of these beliefs comes from investigations on the spot and verified facts, part from the depositions of the various witnesses, part from hypotheses introduced by the detective him
On the use of independence relationships for learning simplified belief networks
 INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
, 1997
"... Belief networks are graphic structures capable of representing dependence and independence relationships among variables in a given domain of knowledge. We focus on the problem of automatic learning of these structures from data, and restrict our study to a specific type of belief network: simple gr ..."
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Cited by 9 (5 self)
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Belief networks are graphic structures capable of representing dependence and independence relationships among variables in a given domain of knowledge. We focus on the problem of automatic learning of these structures from data, and restrict our study to a specific type of belief network: simple graphs, i.e., directed acyclic graphs where every pair of nodes with a common direct child has no common ancestor nor is one an ancestor of the other. Our study is based on an analysis of the independence relationships that may be represented by means of simple graphs. This theoretical study, which includes new characterizations of simple graphs in terms of independence relationships, is the basis of an efficient algorithm that recovers simple graphs using only zero and firstorder conditional independence tests, thereby overcoming some of the practical difficulties of existing algorithms.
A Method of Learning Implication Networks from Empirical Data: Algorithm and MonteCarlo Simulation Based Validation
 IEEE Transactions on Knowledge and Data Engineering
, 1997
"... This paper describes an algorithmic means for inducing implication networks from empirical data samples. The induced network enables efficient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probablistic net ..."
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Cited by 8 (3 self)
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This paper describes an algorithmic means for inducing implication networks from empirical data samples. The induced network enables efficient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probablistic network requirements are relaxed in the construction of dependence relationships based on statistical testing. In order to examine the effectiveness and validity of the induction method, several MonteCarlo simulations were conducted where theoretical Bayesian networks were used to generate empirical data samples \Gamma some of which were used to induce implication relations whereas others were used to verify the results of evidential reasoning with the induced networks. The values in the implication networks were predicted by applying a modified version of DempsterShafer belief updating scheme. The results of predictions were, furthermore, compared to the ones generated by Pearl's stochastic ...
An information retrieval model based on simple Bayesian networks
 International Journal of Intelligent Systems
, 2003
"... In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two ve ..."
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Cited by 7 (4 self)
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In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two versions of an extended model, which also represents direct relationships between documents. In either case the BNs are used to compute efficiently, by means of a new and exact propagation algorithm, the posterior probabilities of relevance of the documents in the collection given a query. The performance of the proposed retrieval models is tested through a series of experiments with several standard document collections. © 2003 Wiley Periodicals, Inc. 1.