An hybrid GA/SVM approach for multiclass classification with directed acyclic graphs

Support Vector Machines constitute a powerful Machine Learning technique originally proposed for the solution of 2-class problems. In the multiclass context, many works divide the whole problem in multiple binary subtasks, whose results axe then combined. Following this approach, one efficient strategy employs a Directed Acyclic Graph in the combination of pairwise predictors in the multiclass solution. However, its generalization depends on the graph formation, that is, on its sequence of nodes. This paper introduces the use of Genetic Algorithms in intelligently searching permutations of nodes in a DAG. The technique proposed is especially useful in problems with relatively high number of classes, where the investigation of all possible combinations would be extremely costly or even impossible.