An Entity Class Model Based Correlated Query Path Selection Method in Multiple Domains

2012 ◽  
pp. 636-643
Author(s):  
Jing Shan ◽  
Derong Shen ◽  
Tiezheng Nie ◽  
Yue Kou ◽  
Ge Yu
Keyword(s):  
Selection Method ◽  
Path Selection ◽  
Class Model ◽  
Model Based ◽  
Multiple Domains ◽  
Entity Class

scholarly journals A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jun-He Yang ◽  
Ching-Hsue Cheng ◽  
Chia-Pan Chan
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Variable Selection ◽  
Water Level ◽  
Selection Method ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Missing Value ◽  
Variable Selection Method ◽  
Model Based

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Candidate Path Selection Method for TCP Performance Improvement in Fixed Robust Routing

2016 ◽  
Vol 5 (6) ◽  
pp. 445-453
Author(s):  
Yukinobu Fukushima ◽  
Takashi Matsumura ◽  
Kazutaka Urushibara ◽  
Tokumi Yokohira
Keyword(s):  
Performance Improvement ◽  
Selection Method ◽  
Path Selection ◽  
Tcp Performance ◽  
Robust Routing ◽  
Candidate Path
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