| Full text | |
| Author(s): |
de Rosa, Gustavo H.
;
Roder, Mateus
;
Passos, Leandro A.
;
Papa, Joao Paulo
Total Authors: 4
|
| Document type: | Journal article |
| Source: | APPLIED SOFT COMPUTING; v. 164, p. 10-pg., 2024-07-29. |
| Abstract | |
Supervised pattern classification relies on a labeled training set to learn decision boundaries that separate samples from different classes. Such samples can be either weakly- or reliably-labeled; in the first case, one can employ techniques specifically designed to cope with uncertainty during labeling, and in the other scenario, it relies on numerous alternatives, including metric learning. Pattern classifiers usually adopt the Euclidean distance to compare samples and assess their proximity, but this implies the feature space is embedded in a plane. However, samples are embedded in curved spaces for some applications, although not straightforward to prove. In this manuscript, we assessed the performance of the Optimum-Path Forest (OPF) classifier under different distance functions, which are used to weigh arcs among samples, for a graph encoding the feature space. This work compared 47 distance measures applied to the OPF classifier considering 22 datasets, plus Decision Trees, Logistic Regression, and Support Vector Machines. The experiments highlighted that OPF is user-friendly when handling distance measures and can obtain better accuracies in some situations than its standard (Euclidean) counterpart and the classifiers mentioned above. On the other hand, time-consuming distance calculations may affect OPF's efficiency during inference. (AU) | |
| FAPESP's process: | 20/12101-0 - Support for computational environments and experiments execution: data acquisition, categorization and maintenance |
| Grantee: | Leandro Aparecido Passos Junior |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |
| FAPESP's process: | 19/02205-5 - Adversarial learning in natural language processing |
| Grantee: | Gustavo Henrique de Rosa |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 23/10823-6 - On the Study and Development of Biological Plausible Computational Intelligent Models |
| Grantee: | Leandro Aparecido Passos Junior |
| Support Opportunities: | Scholarships in Brazil - Support Program for Fixating Young Doctors |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |