Imbalanced Data Over-Sampling Method Based on ISODATA Clustering
Zhenzhe LV,
Qicheng LIU
Abstract:Class imbalance is one of the challenges faced in the field of machine learning. It is difficult for traditional classifiers to predict the minority class data. If the imbalanced data is not processed, the effect of the classifier will be greatly reduced. Aiming at the problem that the traditional classifier tends to the majority class data and ignores the minority class data, imbalanced data over-sampling method based on iterative self-organizing data analysis technique algorithm(ISODATA) clustering is propos… Show more
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