A New Method of Learning Bayesian Networks Structures from Incomplete Data

2005 ◽  
pp. 261-266
Author(s):  
Xiaolin Li ◽  
Xiangdong He ◽  
Senmiao Yuan
Keyword(s):  
Bayesian Networks ◽  
Incomplete Data ◽  
New Method

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Parameter Estimation ◽  
Missing Data ◽  
Incomplete Data ◽  
Model Identification ◽  
Fuzzy Model ◽  
New Method ◽  
Content Type ◽  
Ubiquitous Environment ◽  
Sugeno Model ◽  
Takagi Sugeno

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

A New Method for Estimating Life Distributions from Incomplete Data.

1980 ◽  
Author(s):  
John Kitchin ◽  
Naftali A. Langberg ◽  
Frank Proschan
Keyword(s):  
Incomplete Data ◽  
New Method ◽  
Life Distributions

A New Method for Process Plan Reasoning in Injection Mould Repairs

2010 ◽  
Vol 139-141 ◽  
pp. 1044-1047
Author(s):  
Yi Wang
Keyword(s):  
Bayesian Networks ◽  
Important Task ◽  
New Method ◽  
Injection Mould ◽  
Process Plan ◽  
Reasoning Engine

Mould-repair is an important task in the mould production and depends especially upon the experience of the engineer. It is much full of subjectivity and uncertainty. How to find the knowledge of mould repair and establish the mould repair solving project is an important problem for all the injection mould manufacturers in the industry. Using the Bayesian networks reasoning to help the technician establish the mould-repair solving project was put forward. The mould-repair Bayesian networks (MRBN) reasoning model was established according to the characteristic of mould-repair solving project. The reason control tactic of the process plan system and the reasoning engine of MRBN were designed. The application example about the shrinkage, ill-full injection, gas-obstructed is presented to prove the correctness of the method. The conclusion shows that it can help the engineers analyze the cause of problem well and recommend some more useful solutions.

A NEW-METHOD FOR ESTIMATING LIFE DISTRIBUTIONS FROM INCOMPLETE DATA

1983 ◽  
Vol 1 (3) ◽  
Author(s):  
John Kitchin ◽  
Naftali A. Langberg ◽  
Frank Proschan
Keyword(s):  
Incomplete Data ◽  
New Method ◽  
Life Distributions
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