Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Machine Learning
A comparative assessment of classification methods
Decision Support Systems
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Movie forecast Guru: A Web-based DSS for Hollywood managers
Decision Support Systems
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Predicting box-office success of motion pictures with neural networks
Expert Systems with Applications: An International Journal
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
Artificial Intelligence Review
Sequential manifold learning for efficient churn prediction
Expert Systems with Applications: An International Journal
Direct marketing decision support through predictive customer response modeling
Decision Support Systems
A service oriented architecture to provide data mining services for non-expert data miners
Decision Support Systems
Proceedings of the 51st ACM Southeast Conference
Expert Systems with Applications: An International Journal
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
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Student retention is an essential part of many enrollment management systems. It affects university rankings, school reputation, and financial wellbeing. Student retention has become one of the most important priorities for decision makers in higher education institutions. Improving student retention starts with a thorough understanding of the reasons behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using five years of institutional data along with several data mining techniques (both individuals as well as ensembles), we developed analytical models to predict and to explain the reasons behind freshmen student attrition. The comparative analyses results showed that the ensembles performed better than individual models, while the balanced dataset produced better prediction results than the unbalanced dataset. The sensitivity analysis of the models revealed that the educational and financial variables are among the most important predictors of the phenomenon.