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pridiltal/stray: stray {STReam AnomalY} : Robust Anomaly Detection in Data Streams with Concept Drift
"This package is a modification of HDoutliers package. HDoutliers is a powerful algorithm for the detection of anomalous observations in a dataset, which has (among other advantages) the ability to detect clusters of outliers in multi-dimensional data without requiring a model of the typical behavior of the system. However, it suffers from some limitations that affect its accuracy. In this package, we propose solutions to the limitations of HDoutliers, and propose an extension of the algorithm to deal with data streams that exhibit non-stationary behavior. The results show that our proposed algorithm improves the accuracy, and enables the trade-off between false positives and negatives to be better balanced."

@seealso pridiltal/oddstream
r  stats:time-series  stats:cluster-analysis 
8 hours ago by phnk
Tools for ROC and precision-recall
Classifier evaluation with imbalanced datasets
R  NLP  machineLearning 
4 days ago by pgt150
Learn about using open source R for big data analysis, predictive modeling, data science
blogs  statistics  blog  data  analytics  datascience  r 
4 days ago by shoesiq
Compare outlier detection methods with the OutliersO3 package (Revolutions)
by Antony Unwin, University of Augsburg, Germany There are many different methods for identifying outliers and a lot of them are available in R. But are outliers a matter of opinion? Do all methods give the same results? Articles on outlier methods use a mixture of theory and practice. Theory is all very well, but outliers are outliers because they don’t follow theory. Practice involves testing methods on data, sometimes with data simulated based on theory, better with `real’ dataset...
statistics  library  stats  outliers  visualization  R 
4 days ago by shoesiq

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