Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECML '95 Proceedings of the 8th European Conference on Machine Learning
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
A Comparative Study of Feature-Salience Ranking Techniques
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
Protein classification with multiple algorithms
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Evaluation of diversity measures for binary classifier ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Committee machines for facial-gender recognition
International Journal of Hybrid Intelligent Systems
True Path Rule Hierarchical Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
An efficient hybrid classification algorithm: an example from palliative care
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Evolutionary optimization of regression model ensembles in steel-making process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Exploring the behaviour of base classifiers in credit scoring ensembles
Expert Systems with Applications: An International Journal
Two-level classifier ensembles for credit risk assessment
Expert Systems with Applications: An International Journal
Acute leukemia classification by ensemble particle swarm model selection
Artificial Intelligence in Medicine
Machine learning-based classifiers ensemble for credit risk assessment
International Journal of Electronic Finance
Advanced Engineering Informatics
Combining multiple predictive models using genetic algorithms
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
Hybrid random subsample classifier ensemble for high dimensional data sets
International Journal of Hybrid Intelligent Systems
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The ensemble learning approach has been increasingly used in data mining for improving performance. However, the gain on the learning performance appears varying considerably from application to application. In some cases there were little or no gains achieved even when the same ensemble paradigms were used. This means that there are still some problems in understanding some basic and fundamental issues in ensemble methodology, especially on the factors that can affect the performance of an ensemble and the strategies for constructing effective ensembles. This paper attempts to address these issues. It first describes the possible influencing factors and then focuses on investigating the most important factor - diversity and its relationships with the accuracy of ensemble. In this study, two types of ensembles - homogeneous and heterogeneous ensembles are defined and constructed by using ten different learning algorithms and their diversity and accuracy are evaluated in order to find out which types of ensemble possess high diversity and are thus more accurate. For each of the ten learning algorithms, its ability for generating different types of diversity is estimated quantitatively by using ten common diversity measures and their characteristics are then analyzed to establish their correlation with ensemble performance. The study used fifteen popular data sets to verify the consistence and reliability of our experimental findings.