Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Applying One-Sided Selection to Unbalanced Datasets
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Support Vector Machines with Embedded Reject Option
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
A Recognition-Based Alternative to Discrimination-Based Multi-layer Perceptrons
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Nonlinear programming: a historical view
ACM SIGMAP Bulletin
Facing Imbalanced Classes through Aggregation of Classifiers
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
MSMOTE: Improving Classification Performance When Training Data is Imbalanced
IWCSE '09 Proceedings of the 2009 Second International Workshop on Computer Science and Engineering - Volume 02
On rejecting unreliably classified patterns
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Mitotic HEp-2 cells recognition under class skew
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
A double-ensemble approach for classifying skewed data streams
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Analysis and design of rank-based classifiers
Expert Systems with Applications: An International Journal
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Class imbalance limits the performance of most learning algorithms since they cannot cope with large differences between the number of samples in each class, resulting in a low predictive accuracy over the minority class. In this respect, several papers proposed algorithms aiming at achieving more balanced performance. However, balancing the recognition accuracies for each class very often harms the global accuracy. Indeed, in these cases the accuracy over the minority class increases while the accuracy over the majority one decreases. This paper proposes an approach to overcome this limitation: for each classification act, it chooses between the output of a classifier trained on the original skewed distribution and the output of a classifier trained according to a learning method addressing the course of imbalanced data. This choice is driven by a parameter whose value maximizes, on a validation set, two objective functions, i.e. the global accuracy and the accuracies for each class. A series of experiments on ten public datasets with different proportions between the majority and minority classes show that the proposed approach provides more balanced recognition accuracies than classifiers trained according to traditional learning methods for imbalanced data as well as larger global accuracy than classifiers trained on the original skewed distribution.