Recursive least square perceptron model for non-stationary and imbalanced data stream classification

@article{Ghazikhani2013RecursiveLS,
  title={Recursive least square perceptron model for non-stationary and imbalanced data stream classification},
  author={Adel Ghazikhani and Reza Monsefi and Hadi Sadoghi Yazdi},
  journal={Evolving Systems},
  year={2013},
  volume={4},
  pages={119-131}
}
Classifying non-stationary and imbalanced data streams encompasses two important challenges, namely concept drift and class imbalance. ‘‘Concept drift’’ (or nonstationarity) is changes in the underlying function being learnt, and class imbalance is vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers and is usually observed in two-class datasets. Previous methods for classifying non-stationary and… CONTINUE READING

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Monsefi e-mail: monsefi@um.ac.ir H. Sadoghi Yazdi e-mail: sadoghi@um.ac.ir H

  • Sadoghi Yazdi Center of Excellence on Soft…

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