Comparing Techniques for Multiclass Classification Using Binary SVM Predictors

Multiclass classification using Machine Learning techniques consists of inducing a function f(x) from a training set composed of pairs (xi,yi) where yi ∈ {1,2,...,k}. Some learning methods are originally binary, being able to realize classifications where k = 2. Among these one can mention Support Vector Machines. This paper presents a comparison of methods for multiclass classification using SVMs. The techniques investigated use strategies of dividing the multiclass problem into binary subproblems and can be extended to other learning techniques. Results indicate that the use of Directed Acyclic Graphs is an efficient approach in generating multiclass SVM classifiers.