Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Combining Classifiers with Meta Decision Trees
Machine Learning
Is Combining Classifiers Better than Selecting the Best One
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
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Issues in stacked generalization
Journal of Artificial Intelligence Research
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
We propose a new set of meta-level features to be used for learning how to combine classifier predictions with stacking. This set includes the probability distributions predicted by the base-level classifiers and a combination of these with the certainty of the predictions. We use these features in conjunction with multi-response linear regression (MLR) at the meta-level. We empirically evaluate the proposed approach in comparison to several state-of-the-art methods for constructing ensembles of heterogeneous classifiers with stacking. Our approach performs better than existing stacking approaches and also better than selecting the best classifier from the ensemble by cross validation (unlike existing stacking approaches, which at best perform comparably to it).