For multiclass imbalanced data online prediction, how to design a self-adapted model is a challenging problem. To address this issue, a novel dynamic multi-classification algorithm which uses two-stage game strategy has been put forward. Different from typical imbalanced classification methods, the proposed approach provided a self-updating model quantificationally, which can match the changes of arriving sample chunk automatically. In data generation phase, two dynamic ELMs with game theory are utilized for generating the lifelike minority class to equilibrate the distribution of different samples. In model update phase, both the current prediction performance and the cost sensitivity are taken into consideration simultaneously. According to the suffer loss and the shifty imbalance ratio, the proposed method develops the relationship between new weight and individual model, and an aggregate model of game theory is adopted to calculate the combination weight. These strategies help the algorithm reduce fitting error of sequence fragments. Also, alterative hidden-layer output matrix can be calculated according to the current fragment, thus building the steady network architecture in the next chunk. Numerical experiments are conducted on eight multiclass UCI datasets. The results demonstrate that the proposed algorithm not only has better generalization performance, but also improves the predictive ability of ELM method for minority samples.

对于多类不平衡数据在线预测，如何设计自适应模型是一个具有挑战性的问题。为了解决这个问题，提出了一种新颖的采用两阶段博弈策略的动态多分类算法。与典型的不平衡分类方法不同，该方法量化地提供了一种自更新模型，该模型可以自动匹配到达样本块的变化。在数据生成阶段，利用带有博弈论的两个动态ELM来生成逼真的少数类，以平衡不同样本的分布。在模型更新阶段，同时考​​虑当前的预测性能和成本敏感性。根据遭受损失和变动不平衡率，该方法建立了新权重与个体模型之间的关系，并采用博弈论的集合模型来计算组合权重。这些策略有助于算法减少序列片段的拟合误差。另外，可以根据当前片段计算出备用隐藏层输出矩阵，从而在下一个块中建立稳定的网络体系结构。在八个多类UCI数据集上进行了数值实验。结果表明，该算法不仅具有较好的泛化性能，而且提高了ELM方法对少数样本的预测能力。另外，可以根据当前片段计算出备用隐藏层输出矩阵，从而在下一个块中建立稳定的网络体系结构。在八个多类UCI数据集上进行了数值实验。结果表明，该算法不仅具有较好的泛化性能，而且提高了ELM方法对少数样本的预测能力。另外，可以根据当前片段计算出备用隐藏层输出矩阵，从而在下一个块中建立稳定的网络体系结构。在八个多类UCI数据集上进行了数值实验。结果表明，该算法不仅具有较好的泛化性能，而且提高了ELM方法对少数样本的预测能力。