Research on Building Ecotourism Warning Sensor System Model Based on Support Vector Machine and Grey Relational Analysis

2015 ◽  
Vol 13 (2) ◽  
pp. 169-175
Author(s):  
Xiong Li ◽  
Shengquan Ma
Keyword(s):  
Support Vector Machine ◽  
Grey Relational Analysis ◽  
System Model ◽  
Sensor System ◽  
Support Vector ◽  
Relational Analysis ◽  
Model Based ◽  
Grey Relational

scholarly journals Operation Rule Derivation of Hydropower Reservoirs by Support Vector Machine Based on Grey Relational Analysis

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2518
Author(s):  
Yuxin Zhu ◽  
Jianzhong Zhou ◽  
Hongya Qiu ◽  
Juncong Li ◽  
Qianyi Zhang
Keyword(s):  
Support Vector Machine ◽  
Grey Relational Analysis ◽  
Optimal Operation ◽  
Support Vector ◽  
Operation Model ◽  
Joint Operation ◽  
Relational Analysis ◽  
Operation Rule ◽  
Grey Relational ◽  
Hydropower Reservoirs

In practical applications, the rational operation rule derivation can lead to significant improvements in the middle and long-term joint operation of cascade hydropower stations. The key issue of actual optimal operation is to select effective attributions from the deterministic optimal operation results, however, there is still no general and mature method to solve this problem. Firstly, the joint optimal operation model of hydropower reservoirs considering backwater effects are established. Then, the dynamic programming and progressive optimality algorithm are applied to solve the joint optimal operation model and the deterministic optimization results are obtained. Finally, the grey relational analysis method is applied to select more effective factors from the obtained results as the input of a support vector machine for further operation rule derivation. The Xi Luo-du and Xiang Jia-ba cascade reservoirs in the upper Yangtze river of China are selected as a case study. The results show that the proposed method can obtain better input factors to improve the performance of SVM, and smallest value of root mean square error by the proposed method of Xi Luo-du and Xiang Jia-ba are 94.33 and 21.32, respectively. The absolute error of hydropower generation for Xi Luo-du and Xiang Jia-ba are 2.57 and 0.42, respectively. Generally, this study provides a well and promising alternative tool to guide the joint operation of hydropower reservoir systems.

AN IMPROVEMENT IN SUPPORT VECTOR MACHINE CLASSIFICATION MODEL USING GREY RELATIONAL ANALYSIS FOR CANCER DIAGNOSIS

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Roselina Sallehuddin ◽  
Sh Hafizah Sy Ahmad Ubaidillah ◽  
Azlan Mohd Zain ◽  
Razana Alwee ◽  
Nor Haizan Mohamed Radzi
Keyword(s):  
Support Vector Machine ◽  
Cancer Diagnosis ◽  
Grey Relational Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Relational Analysis ◽  
Ranking Scheme ◽  
Grey Relational

To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48% by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97% by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis.

scholarly journals Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

2022 ◽  
Vol 16 ◽  
pp. 1-8
Author(s):  
Razana Alwee ◽  
Siti Mariyam Hj Shamsuddin ◽  
Roselina Sallehuddin
Keyword(s):  
Support Vector Regression ◽  
Grey Relational Analysis ◽  
Crime Rate ◽  
Consumer Price Index ◽  
Property Crime ◽  
Economic Indicators ◽  
Support Vector ◽  
Features Selection ◽  
Relational Analysis ◽  
Grey Relational

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

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