Failure prediction models: development and comparison between the multivariate discriminant analysis and the support vector machine for Tunisian companies

2021 ◽  
Vol 43 (3) ◽  
pp. 411
Author(s):  
Fayçal Mraihi ◽  
Inane Kanzari
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Prediction Models ◽  
Failure Prediction ◽  
Support Vector ◽  
Multivariate Discriminant Analysis ◽  
Multivariate Discriminant

scholarly journals A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis

2017 ◽  
Vol 1 (3) ◽  
pp. 13-23
Author(s):  
Ehsan ul Hassan ◽  
Zaemah Zainuddin ◽  
Sabariah Nordin
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress ◽  
Prediction Models ◽  
Early Prediction ◽  
Multivariate Discriminant Analysis ◽  
Advantages And Disadvantages ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Precautionary Measures ◽  
Multivariate Discriminant

In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regression model (LRM) is more advantageous than multivariate discriminant analysis (MDA) for better prediction of financial bankruptcy. However, accurate prediction of bankruptcy is beneficial to improve the regulation of companies, to form policies for companies and to take any precautionary measures if any crisis is about to come in future.

The Computer Interlocking Software System Reliability Test Based on the Monte Carlo

2014 ◽  
Vol 614 ◽  
pp. 397-400 ◽  
Author(s):  
Qiong Guan ◽  
Han Qing Tao ◽  
Bin Huang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Prediction Models ◽  
Failure Prediction ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Training Time ◽  
Railway Signal ◽  
Self Learning

The railway switch failure prediction for railway signal equipment maintenance plays an important role. The paper put forward railway switch failure prediction algorithm based on least squares support vector machine, and chose five characteristic indexes composed of railway switch failure prediction models characteristic input vectors. It reduces the dimension of input vectors, shorten the least squares support vector machine training time, and use a pruning algorithm to accelerate the computing speed maintaining a good regression performance at the same time. The experiment proved that railway switch failure prediction algorithm has strong self-learning ability and higher prediction accuracy based on least squares support vector machine. And it can accelerate the speed of switch failure prediction and improve the accuracy and reliability of railway switch failure prediction.

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