First-Order Autonomous Learning Multi-Model Systems for Multiclass Classification tasks

The First-Order Autonomous Learning Multi-Model (ALMMo) system was initially introduced as a regressor which could be easily adapted to a binary classifier. In this paper, an extension of the ALMMo algorithm is proposed, enabling it to tackle multi-class classification tasks, without escalating the computational demand substantially. Thus, this paper highlights the flexibility of the method by increasing its range of capabilities. The proposed extension is tested in 3 benchmark datasets, and the obtained results are presented as a proof of the concept. Furthermore, these results are compared to 2 benchmark methods, those being shallow neural-networks and support vector machines; as well as to the ALMMo-0 classifier.