A Moment Independent Based Importance Measure with Hybrid Uncertainty

2017 ◽  
pp. 213-224
Author(s):  
Xiaobing Shang ◽  
Tao Chao ◽  
Ping Ma
Keyword(s):  
Importance Measure ◽  
Hybrid Uncertainty

scholarly journals Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network

2019 ◽  
Vol 22 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Jinhua Mi ◽  
Yan-Feng Li ◽  
Michael Beer ◽  
Matteo Broggi ◽  
Yuhua Cheng
Keyword(s):  
Bayesian Network ◽  
Importance Measure ◽  
Common Cause ◽  
Common Cause Failures ◽  
Hybrid Uncertainty

Developing a tier-hybrid uncertainty analysis approach for lifecycle impact assessment of a typical high-rise residential building

2021 ◽  
Vol 167 ◽  
pp. 105424
Author(s):  
Mark Kyeredey Ansah ◽  
Xi Chen ◽  
Hongxing Yang ◽  
Lin Lu ◽  
Heng Li
Keyword(s):  
Uncertainty Analysis ◽  
Impact Assessment ◽  
Residential Building ◽  
Analysis Approach ◽  
High Rise ◽  
Hybrid Uncertainty

Reliability and importance measure analysis of networks with shared risk link groups

2021 ◽  
Vol 211 ◽  
pp. 107578
Author(s):  
Radislav Vaisman ◽  
Yuting Sun
Keyword(s):  
Importance Measure ◽  
Measure Analysis ◽  
Shared Risk

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

scholarly journals Surrogate minimal depth as an importance measure for variables in random forests

2019 ◽  
Vol 35 (19) ◽  
pp. 3663-3671 ◽  
Author(s):  
Stephan Seifert ◽  
Sven Gundlach ◽  
Silke Szymczak
Keyword(s):  
Variable Selection ◽  
Supplementary Information ◽  
Predictor Variables ◽  
Importance Measure ◽  
Surrogate Variables ◽  
Machine Learning Approach ◽  
Complex Relationships ◽  
Minimal Depth ◽  
Insight Into ◽  
Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

Analysis of randomly vibrating structures under hybrid uncertainty

2009 ◽  
Vol 31 (11) ◽  
pp. 2677-2686 ◽  
Author(s):  
Giuseppe Carlo Marano ◽  
Emiliano Morrone ◽  
Giuseppe Quaranta
Keyword(s):  
Vibrating Structures ◽  
Hybrid Uncertainty

Labscale hybrid uncertainty analysis (hybrid rocket engines)

1995 ◽  
Author(s):  
B Greiner ◽  
R Frederick, r
Keyword(s):  
Uncertainty Analysis ◽  
Rocket Engines ◽  
Hybrid Rocket ◽  
Hybrid Uncertainty

A Novel Sparse Weighted Least Squares Support Vector Classifier

2013 ◽  
Vol 842 ◽  
pp. 746-749
Author(s):  
Bo Yang ◽  
Liang Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Importance Measure ◽  
Support Vector ◽  
Comparative Experiment ◽  
Digit Recognition ◽  
Calculating Method ◽  
Imbalance Problem ◽  
Support Vector Classifier

A novel sparse weighted LSSVM classifier is proposed in this paper, which is based on Suykens weighted LSSVM. Unlike Suykens weighted LSSVM, the proposed weighted method is more suitable for classification. The distance between sample and classification border is used as the sample importance measure in our weighted method. Based on this importance measure, a new weight calculating function, using which can adjust the sparseness of weight, is designed. In order to solve the imbalance problem, a kind of normalization weights calculating method is proposed. Finally, the proposed method is used on digit recognition. Comparative experiment results show that the proposed sparse weighted LSSVM can improve the recognition correct rate effectively.

Robust design of tuned mass damper with hybrid uncertainty

2021 ◽  
Author(s):  
Dawei Li ◽  
Hesheng Tang ◽  
Songtao Xue
Keyword(s):  
Robust Design ◽  
Tuned Mass Damper ◽  
Mass Damper ◽  
Hybrid Uncertainty

Development and evaluation of an uncertainty importance measure in fault tree analysis

1997 ◽  
Vol 57 (2) ◽  
pp. 143-157 ◽  
Author(s):  
Jae-Gyeun Cho ◽  
Bong-Jin Yum
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Importance Measure ◽  
Tree Analysis ◽  
Uncertainty Importance
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