基于RSBoost算法的不平衡数据分类方法

Classification Method of Imbalanced Data Based on RSBoost

不平衡数据的分类问题在多个应用领域中普遍存在,已成为数据挖掘和机器学习领域的研究热点。提出了一种新的不平衡数据分类方法 RSBoost,以解决传统分类方法对于少数类识别率不高和分类效率低的问题。该方法采用SMOTE方法对少数类进行过采样处理,然后对整个数据集进行随机欠采样处理,以改善整个数据集的不平衡性,再将其与Boosting算法相结合来对数据进行分类。通过实验对比了5种方法在多个公共数据集上的分类效果和分类效率,结果表明该方法具有较高的分类识别率和分类效率。

The problem of class imbalance which is very common to many application domains becomes the research hotspot in data mining and machine learning. We presented a new classification method of imbalance data, called RS- Boost, to increase the recognition rate of minority class and the classification efficiency. This approach uses SMOTE （synthetic minority over-sampling technique） and random under-sampling to balance the data sets, and then uses boos- ting method to optimize the classification performance. We conducted experiments using several public data sets to eva- luate the performances of RSBoost and other four methods. The experimental results show that the approach proposed in this article can improve the classification performance and efficiency of imbalance data sets.