Optimum-Path Forest Theory, Algorithms, and Applications
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 bio...
Uložené v:
| Hlavní autori: | , |
|---|---|
| Médium: | E-kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Chantilly
Elsevier Science & Technology
2022
Academic Press |
| Vydanie: | 1 |
| Predmet: | |
| ISBN: | 9780128226889, 0128226889 |
| On-line prístup: | Získať plný text |
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- 6.2.2.5 Optimum-path forest based on k-connectivity -- 6.3 Methodology -- 6.3.1 Data set -- 6.3.2 Features set -- 6.3.3 Metrics -- 6.3.4 Experimental setup -- 6.4 Experimental results -- 6.4.1 Classification -- 6.4.2 Statistical analysis -- 6.4.3 Computational burden -- 6.5 Conclusions and future works -- References -- 7 Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest -- 7.1 Introduction -- 7.2 Theoretical background -- 7.2.1 Optimum-path forest -- Training step -- Testing step -- 7.2.2 Siamese networks -- 7.3 Methodology -- 7.3.1 Proposed approach -- 7.3.2 Data sets -- 7.3.3 Experimental setup -- 7.4 Experimental results -- 7.4.1 BBC News -- 7.4.2 Caltech101 Silhouettes -- 7.4.3 MPEG-7 -- 7.4.4 Semeion -- 7.5 Conclusion -- References -- 8 An iterative optimum-path forest framework for clustering -- 8.1 Introduction -- 8.2 Related work -- 8.3 The iterative optimum-path forest framework -- 8.3.1 Seed set selection -- 8.3.2 Clustering by optimum-path forest -- 8.3.3 Seed recomputation -- 8.3.4 Returning the forest with lowest total path-cost -- 8.3.5 Algorithm outline -- 8.3.6 Application to object delineation -- 8.4 Experimental results -- 8.4.1 Object delineation by iterative dynamic trees -- 8.4.2 Analysis on road networks -- 8.4.3 Experiments on synthetic data sets -- 8.5 Conclusions and future work -- Acknowledgments -- References -- 9 Future trends in optimum-path forest classification -- References -- Index -- Back Cover
- Front Cover -- Optimum-Path Forest -- Copyright -- Dedication -- Contents -- List of contributors -- Biography of the editors -- Preface -- 1 Introduction -- References -- 2 Theoretical background and related works -- 2.1 Introduction -- 2.2 The optimum-path forest framework -- 2.2.1 Theoretical background -- 2.2.2 Supervised learning -- 2.2.2.1 OPF using complete graph -- 2.2.2.2 OPF using k-nn graph -- 2.2.3 Semisupervised learning -- 2.2.4 Unsupervised learning -- 2.3 Applications -- 2.3.1 Supervised -- 2.3.1.1 Improvements in training -- 2.3.1.2 Improvements in classification -- 2.3.1.3 Variations in learning -- 2.3.1.4 Biological sciences -- 2.3.1.5 Biometrics -- 2.3.1.6 Electrical engineering -- 2.3.1.7 Geosciences and remote sensing -- 2.3.1.8 Image and video analysis -- 2.3.1.9 Materials engineering -- 2.3.1.10 Medicine -- 2.3.1.11 Network security -- 2.3.1.12 Feature selection -- 2.3.1.13 Petroleum exploration -- 2.3.1.14 Other applications -- 2.3.1.15 Voice recognition -- 2.3.2 Semisupervised -- 2.3.3 Unsupervised -- 2.3.3.1 Electrical engineering -- 2.3.3.2 Image and video processing -- 2.3.3.3 Medicine -- 2.3.3.4 Network security -- 2.3.3.5 Remote sensing images -- 2.3.3.6 Other applications -- 2.4 Conclusions and future trends -- Acknowledgments -- References -- 3 Real-time application of OPF-based classifier in Snort IDS -- 3.1 Introduction -- 3.2 Intrusion detection systems -- 3.2.1 Detection approaches in IDS -- 3.2.2 Anomaly detection techniques -- 3.2.3 Types of IDS -- 3.2.4 Open source IDS -- 3.2.4.1 Snort -- 3.3 Machine learning -- 3.3.1 Learning methods -- 3.3.2 Algorithms -- 3.3.2.1 Optimum-path forest -- 3.3.3 Metrics for effectiveness analysis -- 3.4 Methodology -- 3.4.1 CICIDS2017 data set -- 3.4.2 Data set balancing -- 3.4.3 ml_classifiers plugin -- 3.4.3.1 Network traffic flow management
- 3.4.3.2 Classification of network traffic flows -- 3.4.3.3 Plugin configuration -- 3.5 Experiments and results -- 3.5.1 First stage of experiments -- 3.5.1.1 Naive Bayes -- 3.5.1.2 Decision tree -- 3.5.1.3 Random forests -- 3.5.1.4 Support vector machine -- 3.5.1.5 Optimum-path forest -- 3.5.1.6 AdaBoost -- 3.5.1.7 Comparison of classification techniques -- 3.5.2 Second stage of experiments -- 3.5.2.1 DoS slowloris -- 3.5.2.2 DoS SlowHTTPTest -- 3.5.2.3 DoS hulk -- 3.5.2.4 Port scan -- 3.5.2.5 SSH brute force -- 3.6 Final considerations -- 3.6.1 Future works -- Acknowledgments -- References -- 4 Optimum-path forest and active learning approaches for content-based medical image retrieval -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Active learning strategy -- 4.3 Experiments -- 4.3.1 Results and discussion -- 4.4 Conclusion -- 4.5 Funding and acknowledgments -- References -- 5 Hybrid and modified OPFs for intrusion detection systems and large-scale problems -- 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 -- 5.6.1 Partitioning step -- 5.6.2 Sampling step -- 5.7 Enhancement of MOPF using k-medoids algorithm -- References -- 6 Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest -- 6.1 Introduction -- 6.2 Theoretical background -- 6.2.1 Computer-aided diagnosis of atherosclerotic lesions -- 6.2.2 Optimum-path forest -- 6.2.2.1 Optimum-path forest classifier -- 6.2.2.2 Probabilistic optimum-path forest -- 6.2.2.3 Optimum-path forest-based approach for anomaly detection -- 6.2.2.4 Fuzzy optimum-path forest