An algorithmic framework based on the binarization approach for supervised and semi-supervised multiclass problems

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.