Class Binarization

Abstract

Many learning algorithms are only designed to separate two classes from each other. For example, concept-learning algorithms assume positive examples and negative examples (counterexamples) for the concept to learn, and many statistical learning techniques, such as neural networks or support vector machines, can only find a single separating decision surface. One way to apply these algorithms to multi-class problem is to transform the original multi-class problem into multiple binary problems.