C4.5: programs for machine learning
C4.5: programs for machine learning
Intellectual capital: the new wealth of organizations
Intellectual capital: the new wealth of organizations
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Case Generation Using Rough Sets with Fuzzy Representation
IEEE Transactions on Knowledge and Data Engineering
A Hybrid Network Intrusion Detection Technique Using Random Forests
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
Rough sets data analysis in knowledge discovery: a case of Kuwaiti diabetic children patients
Advances in Fuzzy Systems - Regular issue
Consistency of Random Forests and Other Averaging Classifiers
The Journal of Machine Learning Research
Predicting going concern opinion with data mining
Decision Support Systems
Feature selection in bankruptcy prediction
Knowledge-Based Systems
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications: An International Journal
On strategies for imbalanced text classification using SVM: A comparative study
Decision Support Systems
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
Hi-index | 0.07 |
Corporate going-concern opinions are not only useful in predicting bankruptcy but also provide some explanatory power in predicting bankruptcy resolution. The prediction of a firm's ability to remain a going concern is an important and challenging issue that has served as the impetus for many academic studies over the last few decades. Although intellectual capital (IC) is generally acknowledged as the key factor contributing to a corporation's ability to remain a going concern, it has not been considered in early prediction models. The objective of this study is to increase the accuracy of going-concern prediction by using a hybrid random forest (RF) and rough set theory (RST) approach, while adopting IC as a predictive variable. The results show that this proposed hybrid approach has the best classification rate and the lowest occurrence of Types I and II errors, and that IC is indeed valuable for going-concern prediction.