Anomalies Detection Based on the ROC Analysis using Classifiers in Tactical Cognitive Radio Systems: A survey

Author(s):  
Ahmed Moumena

Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis based on the anomaly detection using classifiers for using them in research. In recent years have been increasingly adopted in the machine learning and data mining research communities. This survey gives definitions of the anomaly detection theory and how to use one ROC curve, what a ROC curve, when we use ROC curves.

Author(s):  
Lilian Freitas ◽  
Yomara Pires ◽  
Jefferson Morais ◽  
Joo Costa ◽  
Aldebaro Klautau

Author(s):  
Nicolas Lachiche

Receiver Operating Characteristic (ROC curves) have been used for years in decision making from signals, such as radar or radiology. Basically they plot the hit rate versus the false alarm rate. They were introduced recently in data mining and machine learning to take into account different misclassification costs, or to deal with skewed class distributions. In particular they help to adapt the extracted model when the training set characteristics differ from the evaluation data. Overall they provide a convenient way to compare classifiers, but also an unexpected way to build better classifiers.


2009 ◽  
Vol 24 (2) ◽  
pp. 530-547 ◽  
Author(s):  
Matthew S. Wandishin ◽  
Steven J. Mullen

Abstract Receiver operating characteristic (ROC) curves have become a common analysis tool for evaluating forecast discrimination: the ability of a forecast system to distinguish between events and nonevents. As is implicit in that statement, application of the ROC curve is limited to forecasts involving only two possible outcomes, such as rain and no rain. However, many forecast scenarios exist for which there are multiple possible outcomes, such as rain, snow, and freezing rain. An extension of the ROC curve to multiclass forecast problems is explored. The full extension involves high-dimensional hypersurfaces that cannot be visualized and that present other problems. Therefore, several different approximations to the full extension are introduced using both artificial and actual forecast datasets. These approximations range from sets of simple two-class ROC curves to sets of three-dimensional ROC surfaces. No single approximation is superior for all forecast problems; thus, the specific aims in evaluating the forecast must be considered.


2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii149-ii150
Author(s):  
Sied Kebir ◽  
Teresa Schmidt ◽  
Matthias Weber ◽  
Lazaros Lazaridis ◽  
Norbert Galldiks ◽  
...  

Abstract BACKGROUND Pseudoprogression (PSP) detection in glioblastoma has important clinical implications and remains a challenging task. With the significant advances provided by machine learning (ML) in health care, we investigated the potential of ML in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. METHODS We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. All patients presented imaging findings suspected of PSP/TP on contrast-enhanced MRI. For further diagnostic evaluation, patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. To develop a robust ML model, we trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a training cohort and validated the results on a separate test cohort. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. RESULTS Of the 44 patients included in this study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p=0.010 and p=0.047, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. CONCLUSIONS This study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance compared to conventional ROC analysis.


2012 ◽  
Vol E95-B (4) ◽  
pp. 1190-1197
Author(s):  
Hiromasa FUJII ◽  
Hiroki HARADA ◽  
Shunji MIURA ◽  
Hidetoshi KAYAMA

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