Hierarchical Learning Using Deep Optimum-Path Forest

• URL: http://arxiv.org/abs/2102.09312v1
• Date: Thu, 18 Feb 2021 13:02:40 GMT
• Title: Hierarchical Learning Using Deep Optimum-Path Forest
• Authors: Luis C. S. Afonso, Clayton R. Pereira, Silke A. T. Weber, Christian Hook, Alexandre X. Falc\~ao, Jo\~ao P. Papa
• Abstract summary: Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW.
• Score: 55.60116686945561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.

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