Information Ranking Using Optimum-Path Forest

TL;DR

This paper evaluates Optimum-Path Forest classifiers for learning to rank in information retrieval, demonstrating competitive precision and lower computational load compared to traditional methods like SVM-Rank.

Contribution

It is the first to apply OPF classifiers to the learning to rank task, showing their effectiveness in image retrieval scenarios.

Findings

OPF-based methods achieved competitive precision.

OPF approaches outperformed traditional techniques in computational efficiency.

Experiments confirmed the viability of OPF for ranking tasks.

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.