
Optimum-Path Forest
Theory, Algorithms, and Applications
- 1st Edition - January 6, 2022
- Imprint: Academic Press
- Editors: Alexandre Xavier Falcao, João Paulo Papa
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 6 8 8 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 6 8 9 - 6
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 t… Read more

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Request a sales quoteThe 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.
- Presents the first book on Optimum-path Forest
- Shows how it can be used with Deep Learning
- Gives a wide range of applications
- Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Biography of the editors
- Alexandre Xavier Falcão
- João Paulo Papa
- Preface
- Chapter 1: Introduction
- Abstract
- References
- Chapter 2: Theoretical background and related works
- Abstract
- Acknowledgements
- 2.1. Introduction
- 2.2. The optimum-path forest framework
- 2.3. Applications
- 2.4. Conclusions and future trends
- References
- Chapter 3: Real-time application of OPF-based classifier in Snort IDS
- Abstract
- Acknowledgements
- 3.1. Introduction
- 3.2. Intrusion detection systems
- 3.3. Machine learning
- 3.4. Methodology
- 3.5. Experiments and results
- 3.6. Final considerations
- References
- Chapter 4: Optimum-path forest and active learning approaches for content-based medical image retrieval
- Abstract
- 4.1. Introduction
- 4.2. Methodology
- 4.3. Experiments
- 4.4. Conclusion
- 4.5. Funding and acknowledgments
- References
- Chapter 5: Hybrid and modified OPFs for intrusion detection systems and large-scale problems
- Abstract
- 5.1. Introduction
- 5.2. Modified OPF-based IDS using unsupervised learning and social network concept
- 5.3. Hybrid IDS using unsupervised OPF based on MapReduce approach
- 5.4. Hybrid IDS using modified OPF and selected features
- 5.5. Modified OPF using Markov cluster process algorithm
- 5.6. Modified OPF based on coreset concept
- 5.7. Enhancement of MOPF using k-medoids algorithm
- References
- Chapter 6: Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest
- Abstract
- 6.1. Introduction
- 6.2. Theoretical background
- 6.3. Methodology
- 6.4. Experimental results
- 6.5. Conclusions and future works
- References
- Chapter 7: Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest
- Abstract
- 7.1. Introduction
- 7.2. Theoretical background
- 7.3. Methodology
- 7.4. Experimental results
- 7.5. Conclusion
- References
- Chapter 8: An iterative optimum-path forest framework for clustering
- Abstract
- Acknowledgements
- 8.1. Introduction
- 8.2. Related work
- 8.3. The iterative optimum-path forest framework
- 8.4. Experimental results
- 8.5. Conclusions and future work
- References
- Chapter 9: Future trends in optimum-path forest classification
- Abstract
- References
- Index
- Edition: 1
- Published: January 6, 2022
- No. of pages (Paperback): 244
- No. of pages (eBook): 244
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128226889
- eBook ISBN: 9780128226896
AX
Alexandre Xavier Falcao
JP