Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Model Trees for Classification
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Generalized feature extraction for structural pattern recognition in time-series data
Generalized feature extraction for structural pattern recognition in time-series data
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning Decision Trees for Unbalanced Data
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generating diverse ensembles to counter the problem of class imbalance
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Class Confidence Proportion Decision Trees). Moreover, different ensemble methods are considered using these trees as base classifiers: Bagging, Random Subspaces, AdaBoost, MultiBoost, LogitBoost and specific methods for imbalanced data: Random Undersampling and SMOTE. Ensembles of Model Trees also give better results than ensembles of the other considered trees.