Variable Selection for Mixed Data Clustering: Application in Human Population Genomics

2019 ◽  
Vol 37 (1) ◽  
pp. 124-142 ◽  
Author(s):  
Matthieu Marbac ◽  
Mohammed Sedki ◽  
Tienne Patin
Keyword(s):  
Variable Selection ◽  
Data Clustering ◽  
Human Population ◽  
Population Genomics ◽  
Mixed Data ◽  
Selection For

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For

scholarly journals Flexible variable selection for recovering sparsity in nonadditive nonparametric models

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1155-1163 ◽  
Author(s):  
Zaili Fang ◽  
Inyoung Kim ◽  
Patrick Schaumont
Keyword(s):  
Variable Selection ◽  
Nonparametric Models ◽  
Selection For

A novel heterogeneous ensemble approach to variable selection for gas-liquid two-phase CO2 flow metering

2021 ◽  
Vol 110 ◽  
pp. 103418
Author(s):  
Caiying Sun ◽  
Lijuan Wang ◽  
Yong Yan ◽  
Wenbiao Zhang ◽  
Ding Shao
Keyword(s):  
Variable Selection ◽  
Two Phase ◽  
Ensemble Approach ◽  
Flow Metering ◽  
Selection For ◽  
Co2 Flow ◽  
Heterogeneous Ensemble

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

scholarly journals Variable selection for modeling the absolute magnitude at maximum of Type Ia supernovae

2015 ◽  
Vol 67 (3) ◽  
pp. 55 ◽  
Author(s):  
Makoto Uemura ◽  
Koji S. Kawabata ◽  
Shiro Ikeda ◽  
Keiichi Maeda
Keyword(s):  
Variable Selection ◽  
Absolute Magnitude ◽  
Type Ia Supernovae ◽  
Type Ia ◽  
Selection For ◽  
The Absolute ◽  
Ia Supernovae

Variable selection for discriminating herbal medicines with chromatographic fingerprints

2006 ◽  
Vol 572 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Fan Gong ◽  
Bo-Tang Wang ◽  
Yi-Zeng Liang ◽  
Foo-Tim Chau ◽  
Ying-Sing Fung
Keyword(s):  
Variable Selection ◽  
Herbal Medicines ◽  
Chromatographic Fingerprints ◽  
Selection For
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