一种用于不平衡数据分类的改进AdaBoost算法

真实世界中存在大量的类别不平衡分类问题，传统的机器学习算法如AdaBoost算法，关注的是分类器的整体性能，而没有给予小类更多的关注。因此针对类别不平衡学习算法的研究是机器学习的一个重要方向。AsymBoost作为AdaBoost的一种改进算法，用于类别不平衡学习时，牺牲大类样本的识别精度来提高小类样本的分类性能。AsymBoost算法依然可能遭遇样本权重过大造成的过适应问题。据此提出了一种新型的AdaBoost改进算法。该方法通过对大类中分类困难样本的权重和标签进行处理，使分类器能够同时获得较好的查准率和查全率。实验结果表明，该方法可以有效提高在不平衡数据集上的分类性能。

Novel modified AdaBoost algorithm for imbalanced data classification

Quantities of imbalanced datasets exist in real world and classical learning algorithms attempt to get high precision on the datasets while learning few concepts from the minority class.Research on class-imbalance learning is an important part of machine learning.As an improvement to AdaBoost,AsymBoost improves performance on classification of examples of minority class but decreases accuracy of examples of majority class.AsymBoost may still have the overfitting problem when weights of examples become too large.In this paper a novel modified AdaBoost algorithm is proposed by new reweighting and label-modifying approach to deal with imbalanced datasets and it has shown promising results when compared to AdaBoost and AsymBoost.