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<>1 简介
本文根据2008 IEEE的《ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced
Learning》翻译总结的。
ADASYN: Adaptive Synthetic，自适应合成。根据不同的少数样本其学习的困难程度，使用权重分布，为少数较难学习的类别样本合成更多的训练数据
。看下面算法只是生成了少数较难学习的类别样本。
ADASYN不仅可以减少原始不平衡数据分布导致的学习偏见，而且可以自适应偏移决策边界，使其聚焦于那些较难学习的样本上。
ADASYN支持两分类，也支持多类别分类。
处理不平衡学习问题有下面五个方向：
1）采用策略：过采样（oversampling）、欠采样（undersampling）。
2）合成数据生成：人工生成数据。如SMOTE、SMOTE-Boost、DataBoost-IM。ADASYN就是借鉴了SMOTE、SMOTE-Boost、DataBoost-IM后设计的。
3）成本分析Cost-sensitive学习。为不同错误类型或者实例类型，使用一个成本矩阵（cost-matrix）来帮助学习不平衡数据。也就是说，成本分析学习没有直接修改不平衡数据分布，而是靠使用不同的成本矩阵（cost-matrix）来描述对任一特殊数据错误分类的成本。
4）Active learning.主动学习。
5）Kernel-based 方法.
<>2 ADASYN算法
<>2.1 输入
<>2.2 过程
<>2.3 其他
<>3 实验
<>3.1评测指标
我们采用决策树作为模型。采用Overall Accuracy (OA)、G_mean评价结果。
<>3.2 实验结果
可以看到在G_mean评测下，ADASYN取得了很好的成绩。ADASYN在少数类别和多数类别上都有提升，而不是通过牺牲一个类别来提升另一个类别的准确率。
<>4 代码实现
from imblearn.over_sampling import SMOTE, ADASYN
oversample = ADASYN(random_state=40)
X = train_data_c[features]
Y = train_data_c[‘label’]
X, Y = oversample.fit_resample(X, Y)
