Results 241 to 250 of about 298,037 (275)

Hybrid Markov Blanket discovery

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
In a Bayesian Network (BN), a target node is independent of all other nodes given its Markov Blanket (MB). By finding the MB, many problem can be solved directly or indirectly. There exist predominately two different approaches to finding the MB: the score-based and the constraint-based algorithms.
Tian Gao, Qiang Ji
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Discovery of context-specific Markov blankets

2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2005
The notion of context-specific Markov blankets (CSMB) is a refinement of Markov blankets (MB). An important property of context specific Markov-blankets is that in the worst case they need as many parameters as a model using a standard Markov blanket, but frequently considerably fewer. The result is expected to be a much improved probabilistic model of
Assaf Klein, Solomon Eyal Shimony
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Online Markov Blanket Discovery With Streaming Features

2020 IEEE International Conference on Knowledge Graph (ICKG), 2020
The Markov blanket (MB) in Bayesian networks has attracted much attention since the MB of a target attribute (T) is the minimal feature subset with maximum prediction ability for classification. Nevertheless, traditional MB discovery methods such as IAMB, HITON-MB, and MMMB are not suitable for streaming features.
Dianlong You, Ruiqi Li, Miaomiao Sun, Xinju Ou, Shunpan Liang, Fuyong Yuan   +5 more
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Markov blanket and Markov boundary of multiple variables

J. Mach. Learn. Res., 2018
Summary: Markov blanket (Mb) and Markov boundary (MB) are two key concepts in Bayesian networks (BNs). In this paper, we study the problem of Mb and MB for multiple variables. First, we show that Mb possesses the additivity property under the local intersection assumption, that is, an Mb of multiple targets can be constructed by simply taking the union
Xu-Qing Liu, Xin-sheng Liu
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Markov blankets: Realism and our ontological commitments

Behavioral and Brain Sciences, 2022
Abstract The authors argue that their target is orthogonal to the realism and instrumentalist debate. I argue that it is born directly from it. While the distinction is helpful in illuminating how some ontological commitments demand a theory of implementation, it's less clear whether different views cleanly map onto the epistemic and metaphysical ...
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Local Learning Algorithm for Markov Blanket Discovery

2007
Learning of Markov blanket can be regarded as an optimal solution to the feature selection problem. In this paper, we propose a local learning algorithm, called Breadth-First search of MB (BFMB), to induce Markov blanket (MB) without having to learn a Bayesian network first. It is demonstrated as (1) easy to understand and prove to be sound in theory; (
Shunkai Fu, Michel C. Desmarais
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Selecting Features by Learning Markov Blankets

2007
In this paper I propose a novel feature selection technique based on Bayesian networks. The main idea is to exploit the conditional independencies entailed by Bayesian networks in order to discard features that are not directly relevant for classification tasks.
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Markov Blanket Approximation Based on Clustering

2011
This paper presents new idea for Markov blanket approximation. It uses well known heuristic ordering of variables based on mutual information, but in another way then it was considered in previous works. Instead of using it as a simple help tool in a more complicated method most often based on statistical tests - presented here idea tries to rely ...
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A Fast Markov blanket discovery algorithm

2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014
Learning Markov blanket MB plays an important role in feature selection for classification, causal discovery, and Bayesian Networks learning. In this paper, an efficient and effective algorithm, called Fast Iterative Parent-Child based search of MB (FIPC-MB) is proposed to learn the MB of the target variable T.
Xiaofeng Zhu, Youlong Yang
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Identifying Markov Blankets Using Lasso Estimation

2004
Determining the causal relation among attributes in a domain is a key task in data mining and knowledge discovery. The Minimum Message Length (MML) principle has demonstrated its ability in discovering linear causal models from training data. To explore the ways to improve efficiency, this paper proposes a novel Markov Blanket identification algorithm ...
Gang Li 0009, Honghua Dai 0001, Yiqing Tu   +2 more
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