Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Self-Organizing Maps
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploratory Under-Sampling for Class-Imbalance Learning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Diversity in Combinations of Heterogeneous Classifiers
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Boosting lite: handling larger datasets and slower base classifiers
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Multilabel classification using heterogeneous ensemble of multi-label classifiers
Pattern Recognition Letters
Relationship between diversity and correlation in multi-classifier systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Improving bagging performance through multi-algorithm ensembles
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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Two factors that slow down the deployment of classification or supervised learning in real-world situations. One is the reality that data are not perfect in practice, while the other is the fact that every technique has its own limits. Although there have been techniques developed to resolve issues about imperfectness of real-world data, there is no single one that outperforms all others and each such technique focuses on some types of imperfectness. Furthermore, quite a few works apply ensembles of heterogeneous classifiers to such situations. In this paper, we report a work on progress that studies the impact of heterogeneity on ensemble, especially focusing on the following aspects: diversity and classification quality for imbalanced data. Our goal is to evaluate how introducing heterogeneity into ensemble influences its behavior and performance.