scholarly journals Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification

2014 ◽  
pp. 519-534
Author(s):  
Gherardo Varando ◽  
Concha Bielza ◽  
Pedro Larrañaga
Keyword(s):  
Bayesian Networks ◽  
Expressive Power ◽  
Binary Relevance

Bayesian Networks Based Reliability Analysis of Phased-Mission Systems

2009 ◽  
Vol 31 (10) ◽  
pp. 1814-1825 ◽  
Author(s):  
Dong LIU ◽  
Chun-Yuan ZHANG ◽  
Wei-Yan XING ◽  
Rui LI
Keyword(s):  
Bayesian Networks ◽  
Reliability Analysis

Quantifying Systemic Risk Using Bayesian Networks

2020 ◽  
Author(s):  
Sumit Sourabh ◽  
Markus Hofer ◽  
Drona Kandhai
Keyword(s):  
Bayesian Networks ◽  
Systemic Risk

Recursive generation in language

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Mathematical Logic ◽  
Production System ◽  
General Property ◽  
Recent Literature ◽  
Expressive Power ◽  
The Self ◽  
Recursive Structure ◽  
Embedded Structures ◽  
Mechanical Procedure ◽  
The 1950S

The introduction of recursion into linguistics was the result of applying some of the results of mathematical logic to the study of language. In particular, recursion was introduced in the 1950s as a general property of the mechanical procedure underlying the grammar, in order to account for language’s discrete infinity and expressive power—in the 1950s, this mechanical procedure was a production system, whereas more recently, of course, it is the set-operator merge. Unfortunately, the recent literature has confused the general recursive property of a grammar with specific instances of (recursive) rules/operations within a grammar; more worryingly still, there has been a general conflation of these recursive rules with some of the self-embedded structures these rules can generate, adding to the confusion. The conflation is manifold but always fallacious. Moreover, language manifests a much more generally recursive structure than is usually recognized: bundles of the universal (Specifier)-Head-Complement(s) geometry.

The impact of subway operation on urban traffic: A GRA-BNs based study

2021 ◽  
pp. 1-16
Author(s):  
Lixin Yan ◽  
Tao Zeng ◽  
Yubing Xiong ◽  
Zhenyun Li ◽  
Qingmei Liu
Keyword(s):  
Bayesian Networks ◽  
Operating Characteristic ◽  
Urban Traffic ◽  
Relational Analysis ◽  
Traffic Security ◽  
Structural Network ◽  
Random Decision Forest ◽  
Roc Area ◽  
Grey Relational ◽  
The Impact

With the development of urbanization, urban traffic has exposed many problems. To study the subway’s influence on urban traffic, this paper collects data on traffic indicators in Nanchang from 2008 to 2018. The research is carried out from three aspects: traffic accessibility, green traffic, and traffic security. First, Grey Relational Analysis is used to select 18 traffic indicators correlated with the subway from 22 traffic indicators. Second, the data is discretized and learned based on Bayesian Networks to construct the structural network of the subway’s influence. Third, to verify the reliability of using GRA and the effectiveness of Bayesian Networks (GRA-BNs), Bayesian Networks with full indicators analysis and other four algorithms (Naive Bayes, Random Decision Forest, Logistic and regression) are employed for comparison. Moreover, the receiver operating characteristic (ROC) area, true positive (TP) rate, false positive (FP) rate, precision, recall, F-measure, and accuracy are utilized for comparing each situation. The result shows that GRA-BNs is the most effective model to study the impact of the subway’s operation on urban traffic. Then, the dependence relations between the subway and each index are analyzed by the conditional probability tables (CPTs). Finally, according to the analysis, some suggestions are put forward.

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