Information Ranking Using Optimum-Path Forest
Nenhuma Miniatura disponível
Data
2020-07-01
Autores
Ascencao, Nathalia Q. [UNESP]
Afonso, Luis C. S.
Colombo, Danilo
Oliveira, Luciano
Papa, Joao P. [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
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.
Descrição
Palavras-chave
Como citar
Proceedings of the International Joint Conference on Neural Networks.