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SDM
2012
SIAM
234views Data Mining» more  SDM 2012»
7 years 8 months ago
On Evaluation of Outlier Rankings and Outlier Scores
Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic,...
Erich Schubert, Remigius Wojdanowski, Arthur Zimek...
PAMI
2006
155views more  PAMI 2006»
9 years 5 months ago
Validating a Biometric Authentication System: Sample Size Requirements
Authentication systems based on biometric features (e.g., fingerprint impressions, iris scans, human face images, etc.) are increasingly gaining widespread use and popularity. Ofte...
Sarat C. Dass, Yongfang Zhu, Anil K. Jain
JMLR
2006
134views more  JMLR 2006»
9 years 5 months ago
Considering Cost Asymmetry in Learning Classifiers
Receiver Operating Characteristic (ROC) curves are a standard way to display the performance of a set of binary classifiers for all feasible ratios of the costs associated with fa...
Francis R. Bach, David Heckerman, Eric Horvitz
JMLR
2006
132views more  JMLR 2006»
9 years 5 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
BMCBI
2008
167views more  BMCBI 2008»
9 years 6 months ago
Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments
Background: Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to enviro...
Stefano Parodi, Vito Pistoia, Marco Muselli
NIPS
2007
9 years 7 months ago
Boosting the Area under the ROC Curve
We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be effi
Philip M. Long, Rocco A. Servedio
NIPS
2008
9 years 7 months ago
On Bootstrapping the ROC Curve
This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite...
Patrice Bertail, Stéphan Clémen&cced...
NIPS
2008
9 years 7 months ago
Empirical performance maximization for linear rank statistics
The ROC curve is known to be the golden standard for measuring performance of a test/scoring statistic regarding its capacity of discrimination between two populations in a wide v...
Stéphan Clémençon, Nicolas Va...
NIPS
2008
9 years 7 months ago
Overlaying classifiers: a practical approach for optimal ranking
ROC curves are one of the most widely used displays to evaluate performance of scoring functions. In the paper, we propose a statistical method for directly optimizing the ROC cur...
Stéphan Clémençon, Nicolas Va...
DICTA
2007
9 years 7 months ago
K-means Clustering for Classifying Unlabelled MRI Data
Texture analysis of the liver for the diagnosis of cirrhosis is usually region-of-interest (ROI) based. Integrity of the label of ROI data may be a problem due to sampling. This p...
Gobert N. Lee, Hiroshi Fujita
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