Multi-class Classification

Summary

In machine learning, multiclass or multinomial classification is the problem of classifying instances into more than two classes. While some classification algorithms naturally permit the use of more than two classes, others are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies. Among these strategies are the one-vs.-all (or one-vs.-rest, OvA or OvR) strategy, where a single classifier is trained per class to distinguish that class from all other classes. Prediction is then performed by predicting using each binary classifier, and choosing the prediction with the highest confidence score (e.g., the highest probability of a classifier such as Naive Bayes). Multiclass classification should not be confused with multi-label classification, where multiple classes are to be predicted for each problem instance.

Common Approaches

• Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. [ http://dl.acm.org/citation.cfm?id=1016791 Wu et al]
• Multi-class classification based on asociation rule mining Cowling et al
• Multi-class classification for c4.5 decision trees. Polat et al