| Full text | |
| Author(s): |
de Souza, Renato William R.
[1]
;
de Oliveira, Joao Vitor Chaves
[2, 3]
;
Passos, Jr., Leandro A.
[4]
;
Ding, Weiping
[5]
;
Papa, Joao P.
[4]
;
de Albuquerque, Victor Hugo C.
[1]
Total Authors: 6
|
| Affiliation: | [1] Univ Fortaleza, Grad Program Appl Informat, BR-60811905 Fortaleza, Ceara - Brazil
[2] Pontificia Univ Catolica Rio de Janeiro, BR-22451900 Rio De Janeiro - Brazil
[3] Pontifical Catholic Univ Rio de Janeiro, BR-22451900 Rio De Janeiro - Brazil
[4] Sao Paulo State Univ, BR-01049010 Sao Paulo - Brazil
[5] Nantong Univ, Sch Informat Sci & Technol, Nantong 226019 - Peoples R China
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON FUZZY SYSTEMS; v. 28, n. 12, p. 3076-3086, DEC 2020. |
| Web of Science Citations: | 5 |
| Abstract | |
In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for supervised, semisupervised, and unsupervised learning, named optimum-path forest (OPF), was proposed with competitive results in several applications, besides comprising a low computational burden. In this article, we propose the fuzzy OPF, an improved version of the standard OPF classifier, that learns the samples' membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over 12 public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst case scenarios. (AU) | |
| 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 |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 18/21934-5 - Network statistics: theory, methods, and applications |
| Grantee: | André Fujita |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/19403-6 - Energy-based learning models and their applications |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |
| 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 |