Results 1  10
of
40
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 ..."
Abstract

Cited by 81 (7 self)
 Add to MetaCart
(Show Context)
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
Algorithms for Large Scale Markov Blanket Discovery
 In The 16th International FLAIRS Conference, St
, 2003
"... This paper presents a number of new algorithras for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a loworder polynomial algorit ..."
Abstract

Cited by 33 (4 self)
 Add to MetaCart
This paper presents a number of new algorithras for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a loworder polynomial algorithm and several variants that soundly induce the Markov Blanket under certain broad conditions in datasets with thousands of variables and compare them to other stateoftheart local and global methods with excellent results.
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
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.
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 ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
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
AgentBased Distributed Intrusion Alert System,” to appear
 in Proc. 61h International Workshop on Distributed Computing
"... Abstract. Intrusion detection for computer systems is a key problem in today’s networked society. Current distributed intrusion detection systems (IDSs) are not fully distributed as most of them centrally analyze data collected from distributed nodes resulting in a single point of failure. Increasin ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
Abstract. Intrusion detection for computer systems is a key problem in today’s networked society. Current distributed intrusion detection systems (IDSs) are not fully distributed as most of them centrally analyze data collected from distributed nodes resulting in a single point of failure. Increasingly, researchers are focusing on distributed IDSs to circumvent the problems of centralized approaches. A major concern of fully distributed IDSs is the high false positive rates of intrusion alarms which undermine the usability of such systems. We believe that effective distributed IDSs can be designed based on principles of coordinated multiagent systems. We propose an AgentBased Distributed Intrusion Alert System (ABDIAS) which is fully distributed and provides two capabilities in addition to other functionalities of an IDS: (a) early warning when preattack activities are detected, (b) detecting and isolating compromised nodes by trust mechanisms and votingbased peerlevel protocols. 1
Z.: Structure learning of probabilistic relational models from incomplete relational data. In: ECML
, 2007
"... Abstract. Existing relational learning approaches usually work on complete relational data, but realworld data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract. Existing relational learning approaches usually work on complete relational data, but realworld data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in randomly at first, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibbs sampling is combined with MLT to modify the data and regulate MLT iteratively for obtaining a wellcompleted data set. Finally, probabilistic structure is learned through dependency analysis from the completed data set. Experiments show that the MGDA approach can learn good structures from incomplete relational data. 1
Identifying Behavioral Principles underlying Activity Patterns by means of Bayesian Networks
, 2003
"... Capturing behavioral principles within the context of activitybased travel patterns is of vital importance to build adequate transportation planning models. Of course, there is no solitarily model which is perfectly capable of capturing all behavioral patterns but certain techniques are better suit ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
Capturing behavioral principles within the context of activitybased travel patterns is of vital importance to build adequate transportation planning models. Of course, there is no solitarily model which is perfectly capable of capturing all behavioral patterns but certain techniques are better suited for it than others. In this paper the technique of Bayesian networks is introduced. Bayesian networks are potentially very strong representation techniques since they are capable of capturing the multidimensional nature of complex decisions. Several arguments are presented which clarify why the presented approach is particularly well suited to identify behavioral patterns. To this end and as an empirical study, several significant findings which might exert influence on the choice of transport mode choice were extracted from a large number of potential factors, in the context of a large activity diary dataset. Furthermore, the paper shows a detailed sensitivity analysis report which enables a quantitative evaluation.
Helping Countries Combat Corruption: Progress at the World Bank since
 IN: Electronic proceedings of the 83rd Annual Meeting of the Transportation Research Board
, 1997
"... * corresponding author ..."
(Show Context)
APPLYING BAYESIAN NETWORK AND ASSOCIATION RULE ANALYSIS FOR PRODUCT RECOMMENDATION
"... In recent years, there have been more and more enterprises using Web sites for marketing of various products or services; Internet thus allows customers shopping or searching for information online at any time and any location. If it is possible to recommend products to customers ’ liking at the tim ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
In recent years, there have been more and more enterprises using Web sites for marketing of various products or services; Internet thus allows customers shopping or searching for information online at any time and any location. If it is possible to recommend products to customers ’ liking at the time they are visiting the specific web site, it would reduce the hassle customers experience in searching for products from a large information base. Moreover, it would increase the sales of advertised products. This research tries to make use of modelbased Bayesian network to construct a product recommendation system. This research also uses association rule analysis to assist constructing the Bayesian network structure. This research expects to incorporate the two techniques to improve the shortcoming of a single technique. Especially, visualization of Bayesian network has also being used so that enterprises can easily observe the shopping behaviors of customers towards various products. This research integrates the inference results obtained from individual customers and the entire group in hopes of making surprisingly potential recommendation based on the preferences of the entire group in addition to recommending associated products favored by the given individual.
Control in a 3D Reconstruction System using Selective Perception
"... This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is performed by an independent subsystem that uses Bayesian ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is performed by an independent subsystem that uses Bayesian networks and utility theory to compute the marginal value of information provided by alternative operators and selects the ones with the highest value. We have implemented and tested this control structure with several aerial image datasets. The results show that the knowledge base used by the system can be acquired using standard learning techniques and that the valuedriven approach to the selection of vision algorithms leads to performance gains. Moreover, the modular system architecture simplifies the addition of both control knowledge and new vision algorithms.