Two-Stage Game Strategy for Multiclass Imbalanced Data Online Prediction

摘要

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.