| Full text | |
| Author(s): |
Ascencao, Nathalia Q.
;
Afonso, Luis C. S.
;
Colombo, Danilo
;
Oliveira, Luciano
;
Papa, Joao P.
;
IEEE
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2020-01-01. |
| Abstract | |
The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking accurately and learning to rank are crucial tasks. Context-Based Information Retrieval systems have been of great importance to reduce the effort of finding relevant data. Such systems have evolved by using machine learning techniques to improve their results, but they are mainly dependent on user feedback. Although information retrieval has been addressed in different works along with classifiers based on Optimum-Path Forest (OPF), these have so far not been applied to the learning to rank task. Therefore, the main contribution of this work is to evaluate classifiers based on Optimum-Path Forest, in such a context. Experiments were performed considering the image retrieval and ranking scenarios, and the performance of OPF-based approaches was compared to the well-known SVM-Rank pairwise technique and a baseline based on distance calculation. The experiments showed competitive results concerning precision and outperformed traditional techniques in terms of computational load. (AU) | |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| 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 |
| 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 |