Optimum-Path Forest
Theory, Algorithms, and Applications

Helps readers gain an understanding of the methods, underlying theory and applications of Optimum-Path Forest (OPF)

Alexandre Xavier Falcao (Edited by), João Paulo Papa (Edited by)

9780128226889, Elsevier Science

Paperback, published 24 January 2022

244 pages
22.9 x 15.2 x 1.6 cm, 0.41 kg

The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions.

1. Introduction 2. Theoretical Background and Related Works 3. Real-time application of OPF-based classifier in Snort IDS 4. Optimum-Path Forest and Active Learning Approaches for Content-Based Medical Image Retrieval 5. Hybrid and Modified OPFs for Intrusion Detection Systems and Large-Scale Problems 6. Detecting Atherosclerotic Plaque Calcifications of the Carotid Artery Through Optimum-Path Forest 7. Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest 8. An Iterative Optimum-Path Forest Framework for Clustering 9. Future Trends in Optimum-Path Forest Classification

Subject Areas: Machine learning [UYQM]