Last updated: 2019-08-13
Checks: 6 1
Knit directory: polymeRID/
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Random Forest (RF) is a machine learning algorithm which is based on the traditional concept of decision trees. It’s populat implementation was developed by Breiman (2001). It represents an ensemble classifier which has been reported to be the primary choice between different ensemble classifiers due to its easy handling and high classification accuracies (Sagi and Rokach 2018). More elaborations on scientific background
Different levels of data preprocessing might emphasize different features of the patterns to be learned by an algorithm. To grasp this, different transformations of the data were presented to the RF algorithm. Additionally, the raw data signal was jittered to test which transformation might proove beneficial in delivering high classification accuracies even in the presence of noise. To test this, we define a function which adds noise to the raw data and returns a list with the number of elements equal to the levels of noise applied.
addNoise = function(data, levels = c(0), category="class"){
data.return = list()
index = which(names(data) == category)
for (n in levels){
tmp = as.matrix(data[ , -index])
if (n == 0){
tmp = data
}else{
tmp = as.data.frame(jitter(tmp, n))
tmp[category] = data[category]
}
data.return[[paste("noise", n, sep="")]] = tmp
}
return(data.return)
}
data = read.csv(file = paste0(ref, "reference_database.csv"), header = TRUE)
noisy_data = addNoise(data, levels = c(0,10,100,250,500), category = "class")
# indivitual elements can be selected by using [[ and refering to the index or the name
head(noisy_data[["noise100"]])[1:3,1:3] wvn3992.63003826141 wvn3990.70123147964 wvn3988.77242469788
1 -0.008375491 -0.004206264 -0.009099619
2 -0.002893054 0.013343731 -0.019467560
3 -0.013028379 -0.019465346 -0.003497161Then, in another user defined function which uses the noisy_data objected as input specified data transormations are applied. These are normalization which centers and scales the input data, as well as different forms of the Savitkiy-Golay filter (Savitzky and Golay 1964) and first and second derivative of a raw spectrum. The functions iterates through the noise level elements in the noisy_data object and returns each specified transformation in a list element below the noise level. The exemplary function below applies the pre-processing for normalisation, standard filtering and first derivative only. The implementation of the function used in the project can be found here.
createTrainingSet = function(data, category = "class",
SGpara = list(p=3,w=11), lag = 15){
data.return = list()
for (noise in names(data)){
tmp = as.data.frame(data[[noise]])
classes = tmp[,category]
tmp = tmp[!names(tmp) %in% category]
# original data
data.return[[noise]][["raw"]] = as.data.frame(data[[noise]])
# normalised data
data_norm = preprocess(tmp, type="norm")
data_norm[category] = classes
data.return[[noise]][["norm"]] = data_norm
# SG-filtered data
data_sg = preprocess(tmp, type="sg", SGpara = SGpara)
data_sg[category] = classes
data.return[[noise]][["sg"]] = data_sg
# first derivative of original data
data_rawd1 = preprocess(tmp, type="raw.d1", lag = lag)
data_rawd1[category] = classes
data.return[[noise]][["raw.d1"]] = data_rawd1
}
return(data.return)
}
# applying the function
test_dataset = createTrainingSet(noisy_data, category = "class")
# individual transformations at a certain noise level can be accessed with [[
head(test_dataset[["noise500"]][["raw.d1"]])[1:3,1:3] wvn3963.69793653488 wvn3961.76912975311 wvn3959.84032297134
1 -0.10154790 -0.03910838 0.19501465
2 0.05917357 -0.12878964 0.03169424
3 -0.01021904 -0.12964288 0.08907100The data base of (Primpke et al. 2018) currently shows 1863 variables for each observations. Most of these data points do not bear relevant information to distinguish between differenty types of particles. To shorten the computation time, one can use dimensionality reduction techniques such as principal component analysis (PCA). PCA also has been used to transform spectral data of microplastics in marine ecosystems before (Jung et al. 2018; Lorenzo-Navarro et al. 2018). PCA basically takes the input data for a given number of observation, rotates the axis
Breiman, L. 2001. “Random forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Jung, Melissa R., F. David Horgen, Sara V. Orski, Viviana Rodriguez C., Kathryn L. Beers, George H. Balazs, T. Todd Jones, et al. 2018. “Validation of ATR FT-IR to identify polymers of plastic marine debris, including those ingested by marine organisms.” Marine Pollution Bulletin 127 (December 2017). Elsevier: 704–16. https://doi.org/10.1016/j.marpolbul.2017.12.061.
Primpke, Sebastian, Marisa Wirth, Claudia Lorenz, and Gunnar Gerdts. 2018. “Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy.” Analytical and Bioanalytical Chemistry 410 (21). Analytical; Bioanalytical Chemistry: 5131–41. https://doi.org/10.1007/s00216-018-1156-x.
Sagi, Omer, and Lior Rokach. 2018. “Ensemble learning: A survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (4): 1–18. https://doi.org/10.1002/widm.1249.
Savitzky, Abraham, and Marcel J.E. Golay. 1964. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures.” Analytical Chemistry 36 (8): 1627–39. https://doi.org/10.1021/ac60214a047.
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 19.1
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tensorflow_1.14.0 abind_1.4-5
[3] e1071_1.7-2 keras_2.2.4.1
[5] workflowr_1.4.0.9001 baseline_1.2-1
[7] gridExtra_2.3 stringr_1.4.0
[9] prospectr_0.1.3 RcppArmadillo_0.9.600.4.0
[11] openxlsx_4.1.0.1 magrittr_1.5
[13] ggplot2_3.2.0 reshape2_1.4.3
[15] dplyr_0.8.3
loaded via a namespace (and not attached):
[1] reticulate_1.13 tidyselect_0.2.5 xfun_0.8 purrr_0.3.2
[5] lattice_0.20-38 colorspace_1.4-1 generics_0.0.2 htmltools_0.3.6
[9] yaml_2.2.0 base64enc_0.1-3 rlang_0.4.0 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 foreach_1.4.7 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.3.0 zip_2.0.3 codetools_0.2-16
[21] evaluate_0.14 knitr_1.24 SparseM_1.77 tfruns_1.4
[25] class_7.3-15 Rcpp_1.0.2 scales_1.0.0 backports_1.1.4
[29] jsonlite_1.6 fs_1.3.1 digest_0.6.20 stringi_1.4.3
[33] grid_3.6.1 rprojroot_1.3-2 tools_3.6.1 lazyeval_0.2.2
[37] tibble_2.1.3 crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[41] zeallot_0.1.0 Matrix_1.2-17 assertthat_0.2.1 rmarkdown_1.14
[45] iterators_1.0.12 R6_2.4.0 git2r_0.26.1 compiler_3.6.1