Machine Learning
Ensemble learning via negative correlation
Neural Networks
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Classifying imbalanced data using a bagging ensemble variation (BEV)
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Experimental perspectives on learning from imbalanced data
Proceedings of the 24th international conference on Machine learning
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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Class imbalance learning is an important research area in machine learning, where instances in some classes heavily outnumber the instances in other classes. This unbalanced class distribution causes performance degradation. Some ensemble solutions have been proposed for the class imbalance problem. Diversity has been proved to be an influential aspect in ensemble learning, which describes the degree of different decisions made by classifiers. However, none of those proposed solutions explore the impact of diversity on imbalanced data sets. In addition, most of them are based on resampling techniques to rebalance class distribution, and oversampling usually causes overfitting (high generalisation error). This paper investigates if diversity can relieve this problem by using negative correlation learning (NCL) model, which encourages diversity explicitly by adding a penalty term in the error function of neural networks. A variation model of NCL is also proposed - NCLCost. Our study shows that diversity has a direct impact on the measure of recall. It is also a factor that causes the reduction of F-measure. In addition, although NCL-based models with extreme settings do not produce better recall values of minority class than SMOTEBoost [1], they have slightly better performance of F-measure and G-mean than both independent ANNs and SMOTEBoost and better recall than independent ANNs.