Rough Set-Based Feature Weighted Kernels for Support Vector Machine

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.

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

2010 International Symposium on Computational Intelligence and Design ◽

10.1109/iscid.2010.117 ◽

2010 ◽

Author(s):

Wei Sun ◽

Qiushi Du ◽

Xiaolin Zhao

Keyword(s):

Support Vector Machine ◽

Rough Set ◽

Inner Mongolia ◽

Support Vector ◽

Power Network ◽

Reform Project

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Classification of data with optimized fuzzy rough set feature selection on Nash equilibrium based kernel support vector machine

Malaya Journal of Matematik ◽

10.26637/mjm0s20/0056 ◽

2020 ◽

Vol S (1) ◽

pp. 299-305

Author(s):

Uma Maheshwari J. ◽

Mithra R.

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Nash Equilibrium ◽

Rough Set ◽

Support Vector ◽

Fuzzy Rough Set ◽

Kernel Support Vector Machine

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

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2020.6.3894 ◽

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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