Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Explaining Predictions from a Neural Network Ensemble One at a Time
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
An Approach to Aggregating Ensembles of Lazy Learners That Supports Explanation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Case Representation Issues for Case-Based Reasoning from Ensemble Research
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Porosity Prediction Using Bagging of Complementary Neural Networks
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Criteria Ensembles in Feature Selection
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Artificial Intelligence in Medicine
A new encoding technique for peptide classification
Expert Systems with Applications: An International Journal
Acquiring similarity cases for classification problems
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
On the use of selective ensembles for relevance classification in case-based web search
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Explaining the output of ensembles in medical decision support on a case by case basis
Artificial Intelligence in Medicine
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
A survey of multiple classifier systems as hybrid systems
Information Fusion
| Hi-index | 0.00 |
It is well known that ensembles of predictors produce better accuracy than a single predictor provided there is diversity in the ensemble. This diversity manifests itself as disagreement or ambiguity among the ensemble members. In this paper we focus on ensembles of classifiers based on different feature subsets and we present a process for producing such ensembles that emphasizes diversity (ambiguity) in the ensemble members. This emphasis on diversity produces ensembles with low generalization errors from ensemble members with comparatively high generalization error. We compare this with ensembles produced focusing only on the error of the ensemble members (without regard to overall diversity) and find that the ensembles based on ambiguity have lower generalization error. Further, we find that the ensemble members produced focusing on ambiguity have less features on average that those based on error only. We suggest that this indicates that these ensemble members are local learners.