Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification

TL;DR

This paper compares various distance measures used in supervised Optimum-Path Forest classification to evaluate their impact on performance across different datasets, highlighting OPF's adaptability.

Contribution

It provides a comprehensive comparison of multiple distance measures in OPF, demonstrating how they influence classification performance across diverse domains.

Findings

Certain distance measures significantly improve OPF accuracy.

OPF shows strong adaptability to different datasets with appropriate distance measures.

The study benchmarks OPF against traditional classifiers on standard datasets.

Abstract

Machine Learning has attracted considerable attention throughout the past decade due to its potential to solve far-reaching tasks, such as image classification, object recognition, anomaly detection, and data forecasting. A standard approach to tackle such applications is based on supervised learning, which is assisted by large sets of labeled data and is conducted by the so-called classifiers, such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines, among others. An alternative to traditional classifiers is the parameterless Optimum-Path Forest (OPF), which uses a graph-based methodology and a distance measure to create arcs between nodes and hence sets of trees, responsible for conquering the nodes, defining their labels, and shaping the forests. Nevertheless, its performance is strongly associated with an appropriate distance measure, which may vary according to the dataset's nature. Therefore, this work proposes a comparative study over a wide range of distance measures applied to the supervised Optimum-Path Forest classification. The experimental results are conducted using well-known literature datasets and compared across benchmarking classifiers, illustrating OPF's ability to adapt to distinct domains.