This paper reports a new method (simplified as AE-ELM-SynMin) to create the Synthetic Minority class samples for imbalanced classification based on AutoEncoder Extreme Learning Machine (AE-ELM). AE-ELM-SynMin first trains an AE-ELM which is a special ELM with the same input and output, i.e., the original minority class samples. Second, the crossover, mutation and filtration operations are conducted on the hidden-layer output of AE-ELM and then the synthetic hidden-layer output is obtained. Third, the synthetic minority class samples are created by decoding the synthetic hidden-layer output with output-layer weights of AE-ELM. AE-ELM-SynMin guarantees that the synthetic minority class has the higher information amount than original minority class and meanwhile keeps the consistent probability distribution with the original minority class. The experimental results demonstrate the better imbalanced classification performances of AE-ELM-SynMin in comparison with the regular synthetic minority over-sampling technique (Regular-SMOTE) and its variants, e.g., Borderline-SMOTE, Random-SMOTE, and SMOTE-IPF.

本文报道的新方法（简化为AE-ELM-SynMin）来创建的Syn正题闵基于用于不平衡分类ority类样品甲UTO é ncoder ë的Xtreme大号收入中号氨基酸 (AE-ELM)。AE-ELM-SynMin首先训练一个AE-ELM，它是一个具有相同输入和输出的特殊ELM，即原始少数类样本。其次，对AE-ELM的隐藏层输出进行交叉、变异和过滤操作，得到合成的隐藏层输出。第三，合成少数类样本是通过使用 AE-ELM 的输出层权重解码合成隐藏层输出来创建的。AE-ELM-SynMin 保证合成少数类比原始少数类具有更高的信息量，同时保持与原始少数类一致的概率分布。