Gaining insights into support vector machine pattern classifiers using projection-based tour methods
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Concept-Learning in the Presence of Between-Class and Within-Class Imbalances
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Supervised classification with associative SOM
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Clustering based bagging algorithm on imbalanced data sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Evaluation of a new hybrid algorithm for highly imbalanced classification problems
International Journal of Hybrid Intelligent Systems
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For classification problem, the training data will significantly influence the classification accuracy. When the data set is highly unbalanced, classification algorithms tend to degenerate by assigning all cases to the most common outcome. Hence, it is important to select the suitable training data for classification in the imbalanced class distribution problem. In this paper, we propose cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy in the imbalanced class distribution environment. The basic classification algorithm of neural network model is considered. The experimental results show that our cluster-based under-sampling approaches outperform the other under-sampling techniques in the previous studies.