scholarly journals Analysis and Mapping of Rainfall-Induced Landslide Susceptibility in A Luoi District, Thua Thien Hue Province, Vietnam

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 51 ◽  
Author(s):  
Nguyen Long ◽  
Florimond De Smedt
Keyword(s):  
Landslide Susceptibility ◽  
Factor Model ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
True Positive Rate ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Validation Test ◽  
Positive Rate

Rainfall-induced landslides form an important natural threat in Vietnam. The purpose of this study is to explore regional landslide susceptibility mapping in the mountainous district of A Luoi in Thua Thien Hue Province, where data on the occurrence and causes of landslides are very limited. Three methods are applied to examine landslide susceptibility: statistical index, logistic regression and certainty factor. Nine causative factors are considered: elevation, slope, geological strata, fault density, geomorphic landforms, weathering crust, land use, distance to rivers and annual precipitation. The reliability of the landslide susceptibility maps is evaluated by a receiver operating characteristic curve and the area under the curve is used to quantify and compare the prediction accuracy of the models. The certainty factor model performs best. This model is optimized by maximizing the difference between the true positive rate and the false positive rate. The optimal model correctly identifies 84% of the observed landslides. The results are verified with a validation test, whereby the model is calibrated with 75% randomly selected observed landslides, while the remaining 25% of the observed landslides are used for validation. The validation test correctly identifies 81% of the observed landslides in the training set and 73% of the observed landslides in the validation set.

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

scholarly journals Semi-Supervised Learning for Maximizing the Partial AUC

2020 ◽  
Vol 34 (04) ◽  
pp. 4239-4246
Author(s):  
Tomoharu Iwata ◽  
Akinori Fujino ◽  
Naonori Ueda
Keyword(s):  
Supervised Learning ◽  
Binary Classification ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Unlabeled Data ◽  
Spam Filtering ◽  
Classification Problems ◽  
Partial Area ◽  
Positive Rate

The partial area under a receiver operating characteristic curve (pAUC) is a performance measurement for binary classification problems that summarizes the true positive rate with the specific range of the false positive rate. Obtaining classifiers that achieve high pAUC is important in a wide variety of applications, such as cancer screening and spam filtering. Although many methods have been proposed for maximizing the pAUC, existing methods require many labeled data for training. In this paper, we propose a semi-supervised learning method for maximizing the pAUC, which trains a classifier with a small amount of labeled data and a large amount of unlabeled data. To exploit the unlabeled data, we derive two approximations of the pAUC: the first is calculated from positive and unlabeled data, and the second is calculated from negative and unlabeled data. A classifier is trained by maximizing the weighted sum of the two approximations of the pAUC and the pAUC that is calculated from positive and negative data. With experiments using various datasets, we demonstrate that the proposed method achieves higher test pAUCs than existing methods.

scholarly journals MCS-RF: mobile crowdsensing–based air quality estimation with random forest

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880470 ◽  
Author(s):  
Cheng Feng ◽  
Ye Tian ◽  
Xiangyang Gong ◽  
Xirong Que ◽  
Wendong Wang
Keyword(s):  
Random Forest ◽  
Urban Areas ◽  
Large Scale ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
Estimation Method ◽  
True Positive Rate ◽  
Mobile Crowdsensing ◽  
Fine Grained ◽  
Positive Rate

It is a great challenge to offer a fine-grained and accurate PM2.5 monitoring service in urban areas as required facilities are very expensive and huge. Since PM2.5 has a significant scattering effect on visible light, large-scale user-contributed image data collected by the mobile crowdsensing bring a new opportunity for understanding the urban PM2.5. In this article, we propose a fine-grained PM2.5 estimation method based on random forest with data announced by meteorological departments and collected from smartphone users without any PM2.5 measurement devices. We design and implement a platform to collect data in the real world including the image provided by users. By combining online learning and offline learning, the method based on random forest performs well in terms of time complexity and accuracy. We compare our method with two kinds of baselines: subsets of the whole data sets and six classical models (such as logistic, naive Bayes). Six kinds of evaluation indexes (precision, recall, true-positive rate, false-positive rate, F-measure, and receiver operating characteristic curve area) are used in the evaluation. The experimental results show that our method achieves high accuracy (precision: 0.875, recall: 0.872) on PM2.5 estimation, which outperforms the other methods.

scholarly journals ROC plot and AUC with binary classifiers: pragmatic analysis of cognitive screening instruments

2021 ◽  
Author(s):  
Gashirai K Mbizvo ◽  
Andrew J Larner
Keyword(s):  
Test Performance ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
True Positive Rate ◽  
Cognitive Status ◽  
Test Accuracy ◽  
Cognitive Screening ◽  
Continuous Scale ◽  
Positive Rate ◽  
Binary Classifiers

Receiver operating characteristic (ROC) plots are a performance graphing method showing the relative trade-off between test benefits (true positive rate) and costs (false positive rate) with the area under the curve (AUC) giving a scalar value of test performance. It has been suggested that ROC and AUC may be potentially misleading when examining binary predictors rather than continuous scales. The purpose of this study was to examine ROC plots and AUC values for two binary classifiers of cognitive status (applause sign, attended with sign), a cognitive screening instrument producing categorical data (Codex), and a continuous scale screening test (Mini-Addenbrooke's Cognitive Examination), the latter two also analysed with single fixed threshold tests. For each of these plots, AUC was calculated using different methods. The findings indicate that if categorical or continuous measures are dichotomised then the calculated AUC may be an underestimate, thus affecting screening or diagnostic test accuracy which in the context of clinical practice may prove to be misleading.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

Watch For Failing Objects: What Inappropriate Compliance Reveals About Shared Mental Models In Autonomous Cars

2021 ◽  
Vol 65 (1) ◽  
pp. 643-647
Author(s):  
Yosef S. Razin ◽  
Jack Gale ◽  
Jiaojiao Fan ◽  
Jaznae’ Smith ◽  
Karen M. Feigh
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
False Positive Rate ◽  
Ground Truth ◽  
True Positive Rate ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Autonomous Cars ◽  
Positive Rate ◽  
Dispositional Factors

This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.

scholarly journals Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

2014 ◽  
Author(s):  
Andreas Tuerk ◽  
Gregor Wiktorin ◽  
Serhat Güler
Keyword(s):  
Probability Distributions ◽  
False Positive Rate ◽  
Synthetic Data ◽  
True Positive Rate ◽  
Rna Seq ◽  
Microarray Quality Control ◽  
Data Set ◽  
Rna Transcripts ◽  
Positive Rate ◽  
Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

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