Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach

•It combines DAG with SVM binary classifiers with one-versus-all approach.•To validate UCI Machine Learning Repository and slate tiles data have been used.•Error rates of different models have been analysed.•Results show a good performance of proposed DAG-one-versus-all model.

We describe a new classification methodology based on binary classifiers constructed using support vector machines and applying a one-versus-all approach supported by the use of the directed acyclic graphs. The new methodology, which is computationally less costly because a smaller number of binary classification problems have to be resolved, was validated using UCI Machine Learning Repository data sets. Results point to the improved performance of the proposed model compared to approaches based on the one-versus-one and directed acyclic graph techniques. This new multiclassification strategy successfully applied to a slate tile classification problem produced favourable results compared to other validated techniques.