Results 1  10
of
34
Learning Equivalence Classes Of Bayesian Network Structures
, 1996
"... Approaches to learning Bayesian networks from data typically combine a scoring metric with a heuristic search procedure. Given aBayesian network structure, many of the scoring metrics derived in the literature return a score for the entire equivalence class to which the structure belongs. When ..."
Abstract

Cited by 130 (1 self)
 Add to MetaCart
Approaches to learning Bayesian networks from data typically combine a scoring metric with a heuristic search procedure. Given aBayesian network structure, many of the scoring metrics derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a metric, it is appropriate for the heuristic search algorithm to searchover equivalence classes of Bayesian networks as opposed to individual structures. We present the general formulation of a search space for which the states of the search correspond to equivalence classes of structures. Using this space, anyoneofanumber of heuristic searchalgorithms can easily be applied. We compare greedy search performance in the proposed search space to greedy search performance in a search space for which the states correspond to individual Bayesian network structures. 1
A Transformational Characterization of Equivalent Bayesian Network Structures
, 1995
"... We present a simple characterization of equivalentBayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are able to easily proveseveral new invariant properties of theoretical interest for equivalent structures. Second, we ..."
Abstract

Cited by 94 (1 self)
 Add to MetaCart
We present a simple characterization of equivalentBayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are able to easily proveseveral new invariant properties of theoretical interest for equivalent structures. Second, we use the characterization to derive an efficient algorithm that identifies all of the compelled edges in a structure. Compelled edge identification is of particular importance for learning Bayesian network structures from data because these edges indicate causal relationships when certain assumptions hold. 1
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 ..."
Abstract

Cited by 79 (11 self)
 Add to MetaCart
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...
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. ..."
Abstract

Cited by 52 (0 self)
 Add to MetaCart
This paper was partially presented at the 9th conference on Uncertainty in Artificial Intelligence, July 1993.
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
 In Proceedings of the 13th International Conference on Machine Learning
, 1996
"... This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the dat ..."
Abstract

Cited by 32 (0 self)
 Add to MetaCart
(Show Context)
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the database best. It turns out that when applied with a specific purpose in mind, such as classification, the performance of these network models may be very poor. We demonstrate that Bayesian network models should be created to meet the specific goal or purpose intended for the model. We first present a goaloriented algorithm for constructing Bayesian networks for predicting uncollectibles in telecommunications riskmanagement datasets. Second, we argue and demonstrate that current Bayesian network learning methods may fail to perform satisfactorily in real life applications since they do not learn models tailored to a specific goal or purpose. Third, we discuss the performance of "goal oriented"...
BNT structure learning package: documentation and experiments
 Technical Report FRE CNRS 2645). Laboratoire PSI, Universitè et INSA de Rouen
, 2004
"... Bayesian networks are a formalism for probabilistic reasonning that is more and more used for classification task in datamining. In some situations, the network structure is given by an expert, otherwise, retrieving it from a database is a NPhard problem, notably because of the search space comple ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
Bayesian networks are a formalism for probabilistic reasonning that is more and more used for classification task in datamining. In some situations, the network structure is given by an expert, otherwise, retrieving it from a database is a NPhard problem, notably because of the search space complexity. In the last decade, lot of methods have been introduced to learn the network structure automatically, by simplifying the search space (augmented naive bayes, K2) or by using an heuristic in this search space (greedy search). Most of these methods deal with completely observed data, but some others can deal with incomplete data (SEM, MWSTEM). The Bayes Net Toolbox introduced by [Murphy, 2001a] for Matlab allows us using Bayesian Networks or learning them. But this toolbox is not ’state of the art ’ if we want to perform a Structural Learning, that’s why we propose this package.
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). ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
(Show Context)
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.
Evolutionary learning of dynamic probabilistic models with large time lags
 International Journal of Intelligent Systems
, 2001
"... In this paper, we explore the automatic explanation of Multivariate Time Series (MTS) through learning Dynamic Bayesian Networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of process MTS in order to generate good networks as quickly as possible. We com ..."
Abstract

Cited by 11 (7 self)
 Add to MetaCart
(Show Context)
In this paper, we explore the automatic explanation of Multivariate Time Series (MTS) through learning Dynamic Bayesian Networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of process MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are tested on both synthetic and realworld MTS. We evaluate sample explanations which have been generated from chemical process data using our methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in timedemanding situations. 1.
A Bayesian Morphometry Algorithm
"... Most methods for structurefunction analysis in medical images usually are based on voxelwise statistical tests performed on registered Magnetic Resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
Most methods for structurefunction analysis in medical images usually are based on voxelwise statistical tests performed on registered Magnetic Resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper we propose Bayesian Morphological Analysis (BMA) methods, based on a Bayesiannetwork representation, for the analysis of MR brain images. First, we describe how Bayesian networks can represent probabilistic associations among voxels and clinical (functional) variables. Second, we present a modelselection framework, which generates a Bayesian network that captures structurefunction relationships from MR brain images and functional variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structurefunction association is nonlinear. Our methods successfully identify voxelwise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i. e., ttest and paired ttest) finds only some of these changes in the linearassociation case, and fails in the nonlinearassociation case.
Modal Logics for Representing Incoherent Knowledge
 In Handbook of Defeasible Reasoning and Uncertainty Management
, 1995
"... In this paper we review ways of representing incoherent 'knowledge' in a consistent way, where the use of modal logic and Kripkestyle semantics is put central. Starting with a presentation of the basic modal framework, we discuss the basic modal systems K, KD (with an excursion to the rep ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
In this paper we review ways of representing incoherent 'knowledge' in a consistent way, where the use of modal logic and Kripkestyle semantics is put central. Starting with a presentation of the basic modal framework, we discuss the basic modal systems K, KD (with an excursion to the representation of conflicting norms in deontic logic) and Chellas' minimal modal logic D. Next we look at the epistemic logics KD45, S4 and S5, including the logical omniscience problem and several nonstandard modal logics to overcome this problem. After this we turn to the issue of reasoning by default, where a conflict of defaults (or default beliefs) may arise. We give an epistemic treatment of default reasoning, and treat the way conflicts of defaults can be solved viewed from the more general perspective of resolving conflicts in meta level reasoning. Furthermore, special attention is paid to specificity in default reasoning as a principle to solve these conflicts, for which we develop an extension of Halpern & Moses' theory of honest formulas. Finally, we discuss several numerical modal logics in their capacity of ways of representation of incoherent information.