Last updated: 2019-05-22

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Knit directory: 2019-feature-selection/

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File	Version	Author	Date	Message
Rmd	1cbc8de	pat-s	2019-05-22	Start workflowr project.

Modeling defoliation as a proxy for tree health: Comparison of feature-selection methods across multiple feature sets derived from hyperspectral data

Authors

Patrick Schratz (patrick.schratz@gmail.com)
Jannes Muenchow
Bernd Bischl
Alexander Brenning

Paper

This repository contains the research compendium of our work on comparing algorithms across multiple feature sets and filtering methods (including ensemble filter methods).

Using machine-learning algorithms to model defoliation of Pinus Radiata trees.
keywords
- high-dimensional data handling
- feature selection
- machine-learning
- ecology
Compare filtering methods (ensemble filter methods) across various algorithms and datasets
Predict defoliation to all available plots (24) and the whole Basque Country (at 200 m resolution)

The following directories belong to this project

analysis/01-download.R
analysis/02-hyperspectral-processing.R
analysis/04-data-processing.R
analysis/05-modeling/

How to use

Read the code, access the data

See the analysis directory on GitHub for the source code that generated the figures and statistical results contained in the manuscript. See the data directory for instructions how to access the raw data discussed in the manuscript.

Install the R package

This repository is organized as an R package, providing functions and raw data to reproduce and extend the analysis reported in the publication. Note that this package has been written explicitly for this project and may not be suitable for more general use.

This project is setup with a drake workflow, ensuring reproducibility. Intermediate targets/objects will be stored in a hidden .drake directory.

The R library of this project is managed by packrat. This makes sure that the exact same package versions are used when recreating the project. When calling packrat::restore(), all required packages will be installed with their specific version.

Please note that this project was built with R version 3.5.2 on a Debian 9 operating system. The packrat packages from this project are not compatible with R versions prior version 3.5.0. For reproducibility, it is recommended to replicate the analysis using the included Dockerfile. Instructions can be found ħere. (In general, it should be possible to reproduce the analysis on any other operating system.)

To clone the project, a working installation of git is required. Open a terminal in the directory of your choice and execute:

git clone git@github.com:pat-s/paper_hyperspectral.git

Then start R in this directory and run

packrat::restore()
r_make()

Licenses

Text: CC-BY-4.0 http://creativecommons.org/licenses/by/4.0/

Code: MIT http://opensource.org/licenses/MIT year: 2019, copyright holder: Patrick Schratz

Data: CC0 http://creativecommons.org/publicdomain/zero/1.0/

Notes and resources

The issues tracker is the place to report problems or ask questions
See the repository history for a fine-grained view of progress and changes.
The organisation of this compendium is based on the work of Carl Boettiger and Ben Marwick.

Literature

What to do first (Feature selection or tuning): https://datascience.stackexchange.com/a/19778/24475
Hastie, T., Friedman, J., & Tibshirani, R. (2001). The Elements of Statistical Learning. Springer New York. https://doi.org/10.1007/978-0-387-21606-5 (Kapitel 16)
FSel algs overview: http://featureselection.asu.edu/algorithms.php
De Jay, N., Papillon-Cavanagh, S., Olsen, C., El-Hachem, N., Bontempi, G., & Haibe-Kains, B. (2013). mRMRe: an R package for parallelized mRMR ensemble feature selection. Bioinformatics, 29(18), 2365–2368. https://doi.org/10/f48vxc
Drotár, P., Gazda, J., & Smékal, Z. (2015). An experimental comparison of feature selection methods on two-class biomedical datasets. Computers in Biology and Medicine, 66, 1–10. https://doi.org/10/f72ttc
Drotár, P., Šimoňák, S., Pietriková, E., Chovanec, M., Chovancová, E., Ádám, N., … Biňas, M. (2017). Comparison of Filter Techniques for Two-Step Feature Selection. COMPUTING AND INFORMATICS, 36(3), 597–617. https://doi.org/10/gbntp7
Drotár, P., Gazda, M., & Gazda, J. (2017). Heterogeneous ensemble feature selection based on weighted Borda count. In 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 1–4). https://doi.org/10.1109/ICITEED.2017.8250495
Nie, F., Huang, H., Cai, X., & Ding, C. H. (2010). Efficient and Robust Feature Selection via Joint 2,1-Norms Minimization. In J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 23 (pp. 1813–1821). Curran Associates, Inc. Retrieved from http://papers.nips.cc/paper/3988-efficient-and-robust-feature-selection-via-joint-l21-norms-minimization.pdf

sessionInfo()

R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-7.3.0/openblas-0.3.5-zncvk4jccaqyfl4z3vszaboeps6hyzta/lib/libopenblas_zen-r0.3.5.so

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.3.0 Rcpp_1.0.0      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.3 git2r_0.24.0    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.3.1   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11 
[13] tools_3.5.2     stringr_1.4.0   glue_1.3.0      xfun_0.5       
[17] yaml_2.2.0      compiler_3.5.2  htmltools_0.3.6 knitr_1.21

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