Hierarchical decomposition through "Mental Images" evaluation

Hierarchical Decomposition Methods (HDMs) are techniques that handle multi-class classification problems by breaking them down into smaller, more manageable binary classification tasks, typically achieving better accuracy than flat classification approaches.In this work, a new HDM based on the exploitation of DRASiW "Mental Images" to construct the optimal tree model is presented.Through experiments performed on 26 standard datasets, we show how this approach improves the system classification performance with respect to the classical flat classification.