An algorithmic framework based on the binarization approach for supervised and semi-supervised multiclass problems
An algorithmic framework based on the binarization approach for supervised and semi-supervised multiclass problems
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DOI：
10.1109/ijcnn.2014.6889793
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发表时间：
2014-07
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通讯作者：
Ayon Sen;M. Islam;K. Murase
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文献类型：
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作者：
Ayon Sen;M. Islam;K. Murase
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。这种技术被称为二值化。这些二进制分类器（也称为基本分类器）必须学习的决策边界比多类分类器的决策边界简单得多。但是二值化产生了一个新的问题，称为类不平衡问题。当用于训练的数据集中一个类的数据项相对少于另一个类的数据项时，就会出现类不平衡问题。如果原始数据集本身是不平衡的，这个问题就会变得更加严重。此外，二值化仅在监督分类领域中实现。在本文中，我们提出了一个称为提升和过采样二值化（BBO）的框架。我们的框架可以处理二进制化所产生的类不平衡问题。正如框架的名称所示，这是通过提升和过采样的组合来实现的。BBO框架可以与任何监督分类算法一起使用。此外，与之前使用的任何其他二值化方法不同，我们也将我们的框架应用于半监督分类。BBO框架已经过UCI机器学习库中大量基准数据集的严格测试。实验结果表明，使用BBO框架实现了比传统的二值化方法更高的精度。
Using a set of binary classifiers to solve the multiclass classification problem has been a popular approach over the years. This technique is known as binarization. The decision boundary that these binary classifiers (also called base classifiers) have to learn is much simpler than the decision boundary of a multiclass classifier. But binarization gives rise to a new problem called the class imbalance problem. Class imbalance problem occurs when the data set used for training has relatively less data items for one class than for another class. This problem becomes more severe if the original data set itself was imbalanced. Furthermore, binarization has only been implemented in the domain of supervised classification. In this paper, we propose a framework called Binarization with Boosting and Oversampling (BBO). Our framework can handle the class imbalance problem arising from binarization. As the name of the framework suggests, this is achieved through a combination of boosting and oversampling. BBO framework can be used with any supervised classification algorithm. Moreover, unlike any other binarization approaches used earlier, we apply our framework with semi-supervised classification as well. BBO framework has been rigorously tested with a number of benchmark data sets from UCI machine learning repository. The experimental results show that using the BBO framework achieves a higher accuracy than the traditional binarization approach.