Work

Hierarchical decomposition through "Mental Images" evaluation

Year: 2025

Type: conference-paper

Abstract: 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.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-77

Authors: Gianluca Coda, Massimo De Gregorio, Antonio Sorgente, Paolo Vanacore

Institution: Institute of Applied Science and Intelligent Systems

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409481105

FWCI:

Cites: 11

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Related to: 10