Anomaly DetectionAnomaly Detection

The Anomaly Detection Extension comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets

The Anomaly Detection Extension comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets

=== 4.0.1 ===

  • Fixed a bug where cluster models may create wrong results in applications, since they don't take the cluster sizes from training.
  • Fixed a bug where Isolation Forests were not able to run on values <0.
  • Fixed a bug where Isolation Forests were not storable if they were grouped.

 

=== 4.0.0 ===

Please be aware that this release is not backwards compatible, since attributes got new names!

  • Anomaly models are now serialized using JSON, not java serialization
  • Normal Anomaly models are now IOTablePredictionModels, just like any other learner (except clustering)
  • Since Anomaly models are now IOTablePredictionModels their response is prediction, not a score
  • Univariate models are still Preprocessing models, but their main score is called prediction. all other scores are called prediction(attributeName)
  • Univariate models are now stating the correct covered attributes in their description

=== 3.3.0 ===

Warning: This version is not backwards compatible, since column names changed from v 3.2.0 to 3.3.0 to have one scheme.

  •  Changed the role of outlier scores in all Detect Outlier operators. Its now a confidence (score).
  • Changed the name of the generated score attribute in all Detect Outlier operators. It is now "score"
  • Added a new operator "Generate Outlier Flag" which allows you to discretize the score attributes.

 

=== 3.2.0===

* Added a new operator Detect Outliers (Isolation Forest), which was previously part of Operator Toolbox

* Trees in isolation forests are now trained on a bootstrapped subset of the original data

* Trees in isolation forests can now define the number of features considered in every tree.

=== 3.1.0 ===

* Added a new operator Detect Outliers (Univariate), which was previously part of Operator Toolbox

* Added a new operator Detect Outliers (Time Series)

=== Version 3.0.1 ===

  • Added new operator Detect Outliers (Clustering) which wraps the three outlier detection algorithms
  • Detect Outliers (Clustering) provides a model to be applied on new data.
  • Added a new operator Detect Outliers (rPCA), which also provides a model.
  • Switched to an up-to date LibSVM version. Robust one-class is thus not available anymore. Results may differ slightly.
  • Switched the lib for RNN operator, results may differ.

=== Version 3.0.2 ===

  • Fixed minor bugs

Product Details

Version 4.0.1
File size 3.0 MB
Downloads 139147 (15 Today)139147 downloads
Vendor Markus Goldstein
Category Machine Learning
Released 8/23/22
Last Update 8/23/22 8:46 AM
(Changes)
License AGPL
Product web site https://github.com/Markus-Go/rapidminer-anomalydetection
Rating 0.0 stars(0)