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IOS

a new representative subset selection and outlier detection method IOS (Isolation forest Outlier detection and Subset selection) has been proposed.IOS can detect outlier and select representative subset of samples simultaneously without y values and reduce prediction errors significantly compared with KS, SPXY and RS methods. The IForest is implemented in R language. All the chemometric methods including IOS for processing datasets are implemented by our research group in MATLAB language. IOS

installation

Install from Local ZIP###

Install the downloaded packages from local zip or tar.gz file.

To start running this algorithm, load the IsolationForest package through "library(IsolationForestt)" in the R commandline windows

###How to cite:###

Example

Milk data contains one outlier and IOS can be used to detect it and select representative subset.

Contact

For any questions, please contact: Zhi-Min Zhang: chenworuo@csu.edu.cn

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