README.md

In rgmantovani/MfeatExtractor: Extract meta-features from classification datasets

MfeatExtractor

Meta-features Extractor ('MfeatExtractor') is an automated code to extract meta-features from classification datasets. The current version covers the following set of data descriptors:

• Statlog meta-features (STAT): measures originally proposed in the STATLOG Project [01], which contains: simple, statistical, model-based, information-theoretic, landmarking and runtime measures. They are extracted via mfe R package [02].

• Data complexity meta-features (COMP): complexity measures of classification problems considering, for example, the overlap in the feature values, the separability of classes, and geometric-topological data properties. The documentation of the corresponding library (DCoL) [03] give more details about them.

• Complex Network meta-features (CNET): measures extracted from a complex network built in the data instances. More details can be found in [04].

• PCA meta-features (PCA): meta-features extracted via a simple PCA process, and originally explored for Data Streams in [05].

Technical Requirements

• R version >= 3.1.0
• Data Complexity Library in C++ (DCoL): http://dcol.sourceforge.net/

Setup

First, download and compile the DCol library and place the resultant binary file (dcol) into the src subdirectory. If the src folder does not exist, create it, and save the binary file there. This binary will be used to compute data complexity meta-features.

The current code depends on the mfe package branch direct. Installing it via R is possible through the command:

devtools::install_github("rivolli/mfe", ref="direct")

Then, after this requirements are satisfied, please install this projects via:

devtools::install_github("rgmantovani/MfeatExtractor")

Running the code

Running a single job is possible via the command:

R CMD BATCH --no-save --no-restore '--args' --datafile=<datafile> --option=<option> mainExtraction.R out.log &

# For example:
# R CMD BATCH --no-save --no-restore '--args' --datafile="iris" --option="all" mainExtraction.R out.log &

It will call the extraction fot the <datafile> specifying with features should be extracted (option). Datafile must be any valid dataset arff file placed at the data/datasets/ sub-directory. It the folder does not exist, please, create one, and place your files in it. The available choices for the optionsparameter are: "mfe" : extracts all meta-features from mfe package; "comp": extracts all the data complexity meta-features; "cnet": extracts all the complex-network-based meta-features; "pca" : extracts all the pca-based meta-features.

The resultant files will be stored and saved at the mfeats sub-directory, generating one sub-folder per dataset.

Joining results and having an overview

We also provide two scripts. One of them returns an overview of the meta-features already extracted from datasets placed at the data/datasets/sub directory:

Rscript overview.R &

The second will join all the available meta-features and returns a data.frame:

Rscript joinMfeats.R &

Contact

Rafael Gomes Mantovani ([e-mail]) University of São Paulo - São Carlos, Brazil.

References

[01] D. Michie, D.J. Spiegelhalter, C.C. Taylor, J. Campbell. Machine Learning, Neural and Statistical Classification, 1994, Ellis Horwood.

[02] Rivolli, A., Garcia, L. P. F., de Carvalho, A. C. P. L. F. (2017). mfe: Meta-Feature Extractor. R package version 0.1.0. http://CRAN.R-project.org/package=mfe

[03] A. Orriols-Puig, N. Maciá, T.K. Ho, Documentation for the data complexity library in C++, Tech. rep., La Salle - Universitat Ramon Llull (2010).

[04] L.P.F. Garcia, A.C.P.L.F. de Carvalho, A.C. Lorena. Noise detection in the meta-learning level. Neurocomputing, v.176, 2016, p.14-25.

[05] Add Tomas paper ref

[ADD citation]

If you use our code/experiments in your research, please, cite our paper where it was firstly used :

@article{Mantovani2017,
  author  = {},
  title   = {},
  journal = {},
  year    = {2017},
  pages   = {}
}

[ADD bibtex entry]

rgmantovani/MfeatExtractor documentation built on May 27, 2019, 1:45 p.m.