New 2-Tier Multiclass Prediction Framework

In multiclass classification problems we face the challenge of having many binary classifiers. Consulting this large number of classifiers might be confusing and time consuming. In this paper, we propose a new framework for training and prediction in multiclass problems. In this framework, we perform traditional training. Next we map training examples to prediction models. Finally we produce the Example Classifier (EC). In prediction a new example is passed through the EC to determine the appropriate classifier which in turn makes the last prediction decision. We conduct experiments comparing our framework with one-VS-one and Directed Acyclic Graph (DAG) using Support Vector Machines. Additionally, we compare our model with well-known ensemble models, namely, AdaBoost and Bagging, Our results indicate that prediction accuracy is comparable to other methodologies with the advantage of consuming less prediction time.