The Use of Background Knowledge in Decision Tree Induction
Machine Learning
Computer
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On the k-NN performance in a challenging scenario of imbalance and overlapping
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
Some Remarks on Chosen Methods of Classifier Fusion Based on Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Integrating selective pre-processing of imbalanced data with Ivotes ensemble
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Evolutionary Computation for Modeling and Optimization
Evolutionary Computation for Modeling and Optimization
Expert Systems with Applications: An International Journal
Designing fusers on the basis of discriminants – evolutionary and neural methods of training
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Identification of different types of minority class examples in imbalanced data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Combining diverse one-class classifiers
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
IEEE Transactions on Neural Networks
Oversampling methods for classification of imbalanced breast cancer malignancy data
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets.