Original Contribution: Stacked generalization
Neural Networks
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cost complexity-based pruning of ensemble classifiers
Knowledge and Information Systems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
EROS: Ensemble rough subspaces
Pattern Recognition
Music-Inspired Harmony Search Algorithm: Theory and Applications
Music-Inspired Harmony Search Algorithm: Theory and Applications
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
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In recent years, a number of works proposing the combination of multiple classifiers to produce a single classification have been reported. The resulting classifier, referred to as an ensemble classifier, is generally found to be more accurate than any of the individual classifiers making up the ensemble. In an ensemble of classifiers, it is hoped that each individual classifier will focus on different aspects of the data and error under different circumstances. By combining a set of so-called base classifiers, the deficiencies of each classifier may be compensated by the efficiency of the others. Ensemble pruning deals with the reduction of an ensemble of predictive models in order to improve its efficiency and performance. Ensemble pruning can be considered as an optimization problem. In our work we propose the use of Harmony search, a music inspired algorithm to prune and select the best combination of classifiers. The work is compared with AdaBoost and Bagging among other popular ensemble methods and our method is shown to perform better than the other methods. We have also compared our work with an ensemble pruning technique based on genetic algorithm and our model has shown better accuracy.