Outlier Detection System
An Outlier Detection System is a detection system that implements an outlier detection algorithm to solve an outlier detection task.
- AKA: Anomaly Detector.
- Context:
- It can range from being a Univariate Outlier Detection System to being a Multivariate Outlier Detection system (such as a bivariate outlier detection system).
- It can range from being a Numerical Outlier Detection System to being an Ordinal Outlier Detection System to being a Categorical Outlier Detection System.
- It can range from being a Unordered-Data Outlier Detection System to being a Sequential-Data Outlier Detection System (such as a temporal outlier detection system).
- It can range from being a I.I.D. Outlier Detection System to being a Non-I.I.D. Outlier Detection System.
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- Example(s):
- Counter-Example(s):
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- See: Outlier Mining System.
References
2020
- https://sites.google.com/view/wsdm2020dlad
- ABSTRACT: Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires the researchers developers to learn the complex structure from noisy data, identify the dynamic anomaly patterns and detect anomalies while lacking sufficient labels. R
2016
- https://www.ucl.ac.uk/molecular-medicine/key-publications/microarray-outlier-analysis
- QUOTE: This software implements an outlier detection strategy that was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance.
The software provides a user friendly interface that enables identification of gross abnormalities in gene expression (outliers) in individuals with complex disorders, using whole genome microarray data.
Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual samples.
The software is implemented using R and java on the Windows OS.
Installation instructions, sample datasets, executables, source code, a user guide and other technical information are provided with the software - see below.
- QUOTE: This software implements an outlier detection strategy that was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance.