基于最大平衡度的自适应随机抽样算法.

The problem on the classification algorithm of imbalanced datasets was analyzed. Common methods of balancing data， including improvement of datasets and the improved algorithm， were summarized. Then a novel algorithm called adaptive random sampling algorithm was put forward based on balance maximization. The classification effect of random forest algorithm was further optimized. Experiments show that the proposed algorithm performs well with the imbalanced data， the new data are fitted with the original data， and it could improve the ability of classifier to deal with the imbalanced data. [ABSTRACT FROM AUTHOR]