A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic
This work addresses classification accuracy for researchers in pattern recognition, but it is incremental as it builds on the existing OPF framework.
The paper tackles the problem of improving classification accuracy in Optimum-Path Forest (OPF) by incorporating fuzzy logic to learn sample memberships unsupervised and use them during supervised training, resulting in robust performance across twelve public datasets with similar worst-case behavior to standard OPF.
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 both supervised, semi-supervised, and unsupervised learning named Optimum-Path Forest (OPF) was proposed with competitive results in several applications, besides comprising a low computational burden. In this paper, we propose the Fuzzy Optimum-Path Forest, 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 twelve public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst-case scenarios.