Pairwise Classification as an Ensemble Technique
ECML '02 Proceedings of the 13th 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
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Combining Multiple Models with Meta Decision Trees
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Boosting recombined weak classifiers
Pattern Recognition Letters
On combining multiple classifiers using an evidential approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Issues in stacked generalization
Journal of Artificial Intelligence Research
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Artificial Intelligence Review
A framework for selection and fusion of pattern classifiers in multimedia recognition
Pattern Recognition Letters
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Combination of multiple classifiers, commonly referred to as an classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. One popular approach to building such a combination of classifiers is known as stacking and is based on a meta-learning approach. In this work we investigate a modified version of stacking based on cluster analysis. Instances from a validation set are firstly classified by all base classifiers. The classified results are then grouped into a number of clusters. Two instances are considered as being similar if they are correctly/incorrectly classified to the same class by the same group of classifiers. When classifying a new instance, the approach attempts to find the cluster to which it is closest. The method outperformed individual classifiers, classification by a clustering method and the majority voting method.