The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Exon/Intron Boundaries Using Dynamic Ensembles
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A Comparison of Several Ensemble Methods for Text Categorization
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dynamic Classifier Selection Method to Build Ensembles using Accuracy and Diversity
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Ensemble with neural networks for bankruptcy prediction
Expert Systems with Applications: An International Journal
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
An experimental bias-variance analysis of SVM ensembles based on resampling techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of multiple classifier systems as hybrid systems
Information Fusion
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Ensemble learning is a method to improve the performance of classification and prediction algorithms. Many studies have demonstrated that ensemble learning can decrease the generalization error and improve the performance of individual classifiers and predictors. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes a genetic algorithm-based coverage optimization technique in the purpose of resolving multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed coverage optimization algorithm can help to design a diverse and highly accurate classification system.