FRaC-package: Feature Regression and Classification Anomaly Detection...

In Stevo15025/FRaC: Anomaly Detection

Description

FRaC is a general approach to the anomaly detection problem that is, the task of identifying instances that come from a different class or distribution than the majority (unsupervised anomaly detection) or a set of verified "normal" data (semi-supervised anomaly detection).The key to making this approach work is to precisely quantify the amount of evidence provided by each observation. To this end, FRaC developes a novel, information-theoretic anomaly measure that combines the contributions of all feature models.

Details

Package:	FRaC
Type:	Package
Version:	0.0.1
Date:	2015-05-01
License:	GPL 3.0

Author(s)

Who wrote it

Maintainer: Who to complain to <[e-mail]> ~~ The author and/or maintainer of the package ~~

References

K. Noto, C. E. Brodley, and D. Slonim. FRaC: A Feature-Modeling Appraoch for Semi-Supervised and Unsupervised Anomaly Detection. Data Mining and Knowledge Discovery, 25(1), pp.109—133, 2011.

Stevo15025/FRaC documentation built on May 9, 2019, 3:08 p.m.