Multilayer feedforward networks are universal approximators
Neural Networks
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
The nature of statistical learning theory
The nature of statistical learning theory
Entropy of English text: experiments with humans and a machine learning system based on rough sets
Information Sciences: an International Journal - From rough sets to soft computing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Predicting business failure using forward ranking-order case-based reasoning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Enhanced default risk models with SVM+
Expert Systems with Applications: An International Journal
Machine learning approach for finding business partners and building reciprocal relationships
Expert Systems with Applications: An International Journal
Elucidating clinical context of lymphopenia by nonlinear modelling
Expert Systems with Applications: An International Journal
Data envelopment analysis classification machine
Information Sciences: an International Journal
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
Rough sets and neural networks based aerial images segmentation method
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Applying case based reasoning for prioritizing areas of business management
Expert Systems with Applications: An International Journal
Company failure prediction in the construction industry
Expert Systems with Applications: An International Journal
Going-concern prediction using hybrid random forests and rough set approach
Information Sciences: an International Journal
Hi-index | 12.07 |
The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. While the efficiency of a corporation's management is generally acknowledged to be a key contributor to corporation's bankrupt, it is usually excluded from early prediction models. The objective of the study is to use efficiency as predictive variables with a proposed novel model to integrate rough set theory (RST) with support vector machines (SVM) technique to increase the accuracy of the prediction of business failure. In the proposed method (RST-SVM), data envelopment analysis (DEA) is employed as a tool to evaluate the input/output efficiency. Furthermore, by RST approach, the redundant attributes in multi-attribute information table can be reduced, which showed that the number of independent variables was reduced with no information loss, is utilized as a preprocessor to improve business failure prediction capability by SVM. The effectiveness of the methodology was verified by experiments comparing back-propagation neural networks (BPN) approach with the hybrid approach (RST-BPN). The results shows that DEA do provide valuable information in business failure predictions and the proposed RST-SVM model provides better classification results than RST-BPN model, no matter when only considering financial ratios or the model including both financial ratios and DEA.