Evaluation of Rural Power Network Reform Project Based on Rough Set and Support Vector Machine in Inner Mongolia

2010 ◽  
Author(s):  
Wei Sun ◽  
Qiushi Du ◽  
Xiaolin Zhao
Keyword(s):  
Support Vector Machine ◽  
Rough Set ◽  
Inner Mongolia ◽  
Support Vector ◽  
Power Network ◽  
Reform Project

Indian Stock Market Prediction Based on Rough Set and Support Vector Machine Approach

2020 ◽  
pp. 345-355
Author(s):  
Rudra Kalyan Nayak ◽  
Ramamani Tripathy ◽  
Debahuti Mishra ◽  
Vijay Kumar Burugari ◽  
Prabha Selvaraj ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Stock Market ◽  
Rough Set ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Indian Stock Market

PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

2011 ◽  
Vol 28 (01) ◽  
pp. 95-109 ◽  
Author(s):  
YU CAO ◽  
GUANGYU WAN ◽  
FUQIANG WANG
Keyword(s):  
Support Vector Machine ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Rough Set Theory ◽  
Listed Companies ◽  
Support Vector ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

scholarly journals A Rough Set and Cellular Genetic Fusion Algorithm for Acute Critical Disease Prediction

2020 ◽  
Vol 15 (6) ◽  
Author(s):  
Hongxin Wang ◽  
Lijing Jia ◽  
Heng Zhuang ◽  
Xueyan Li ◽  
Yuzhuo Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Rough Set ◽  
Rough Set Theory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Disease Prediction ◽  
Fusion Algorithm

This study is to solve the problems of an overly-broad scale of medical indicators, lack of retrospective research samples, insufficient depth of data mining, and low disease prediction accuracy. In this paper, we propose an intelligent screening algorithm that combines a genetic algorithm, cellular automata, and rough set theory. This algorithm can achieve high accuracy in predicting patient outcomes with a small number of indicators. And we compare it with the traditional genetic algorithm. We built the prediction model with 64 indicators based on the logistic regression (AUC 0.8628), support vector machine (AUC 0.5319), Naïve Bayes (AUC 0.7102), and AdaBoost algorithms (AUC 0.9095). Using the cellular genetic algorithm for attribute screening not only effectively reduces the number of indicators but also achieve almost the same accuracy of prediction with 8 indicators based on the logistic regression (AUC 0.8782), support vector machine (AUC 0.8525), Naïve Bayes (AUC 0.8408), and AdaBoost algorithms (AUC 0.8770). Compared with the traditional scoring system, the predictive model established in this paper can more accurately predict rebleeding accidents based on physiological test indicators and continuous patient indicators.

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