IEEE Trans Cybern. 2016 May;46(5):1078-91. doi: 10.1109/TCYB.2015.2423295. Epub 2015 Apr 30.
Using a set of binary classifiers to solve multiclass classification problems has been a popular approach over the years. The decision boundaries learnt by binary classifiers (also called base classifiers) are much simpler than those learnt by multiclass classifiers. This paper proposes a new classification framework, termed binarization with boosting and oversampling (BBO), for efficiently solving multiclass classification problems. The new framework is devised based on the one-versus-all (OVA) binarization technique. Unlike most previous work, BBO employs boosting for solving the hard-to-learn instances and oversampling for handling the class-imbalance problem arising due to OVA binarization. These two features make BBO different from other existing works. Our new framework has been tested extensively on several multiclass supervised and semi-supervised classification problems using five different base classifiers, including neural networks, C4.5, k -nearest neighbor, repeated incremental pruning to produce error reduction, support vector machine, random forest, and learning with local and global consistency. Experimental results show that BBO can exhibit better performance compared to its counterparts on supervised and semi-supervised classification problems.
多年来，使用一组二进制分类器来解决多类分类问题一直是一种流行的方法。与多类分类器相比，二进制分类器（也称为基础分类器）学习到的决策边界要简单得多。本文提出了一种新的分类框架，称为提升和过采样的二进制化（BBO），用于有效地解决多类分类问题。该新框架是基于一对一（OVA）二进制化技术设计的。与大多数以前的工作不同，BBO 采用提升来解决难以学习的实例，并采用过采样来处理由于 OVA 二进制化而产生的类不平衡问题。这两个特性使 BBO 有别于其他现有工作。我们的新框架已经在使用五种不同的基础分类器（包括神经网络、C4.5、k-最近邻、重复增量剪枝以产生错误减少、支持向量机、随机森林和具有局部和全局一致性的学习）的几个多类监督和半监督分类问题上进行了广泛的测试。实验结果表明，BBO 在监督和半监督分类问题上的性能优于其同类。
