Last updated: 2019-09-19
Checks: 7 0
Knit directory: polymeRID/
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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 75bc270 | goergen95 | 2019-09-05 | Build site. |
| Rmd | a848def | goergen95 | 2019-09-05 | changed citation style |
| html | c26a428 | goergen95 | 2019-08-22 | Build site. |
| Rmd | 7e9eddd | goergen95 | 2019-08-22 | wflow_publish(files = c(“analysis/cnn_crossvalidation.Rmd”, “analysis/cnn_exploration.Rmd”, |
| html | f2ee83c | goergen95 | 2019-08-19 | Build site. |
| html | 8c2286f | goergen95 | 2019-08-19 | added calibration.html |
| Rmd | d960dc2 | goergen95 | 2019-08-19 | included calibration |
For the calibration a decision fusion between the best performing models during the exploration stage was implemented. Since SVM-based models did not achieve very high accuracies we only included two RF models and two CNNs. The RF models yielding to the highest accuracies were trained with the raw data and the Savitzkiy-Golay smoothed data. For the CNNs we observed the highest accuracies with the second derivative of the raw data and with the second derivative of the normalized data. These models will be used during calibration. To gain an accuracy value for the fusion approach, again a cross-validation will be used.
The cross-validation of the decision fusion was conducted on ten folds and repeated five times. The complete code can be found here. For every fold, four models are trained and evaluated against a 50% test split. The final class decision is then achieved by combining the probability output of each model and assigning the class with the highest overall probability.
classRFRaw = as.character(predict(rfModRaw, pcaRaw_testing))
propRFRaw = predict(rfModRaw, pcaRaw_testing, type = "prob")
classRFSG = as.character(predict(rfModSG, pcaSG_testing))
propRFSG = predict(rfModSG, pcaSG_testing, type = "prob")
classCNND2 = as.character(classes[keras::predict_classes(cnnD2, x_testD2)+1])
propCNND2 = keras::predict_proba(cnnD2, x_testD2)
classCNNND2 = as.character(classes[keras::predict_classes(cnnND2, x_testND2)+1])
propCNNND2 = keras::predict_proba(cnnND2, x_testND2)
# probability
probs = (propRFRaw + propRFSG + propCNND2 + propCNNND2) / 4
pred = lapply(1:nrow(probs), function(x){
which.max(probs[x,])
})
predVals = lapply(1:nrow(probs), function(x){
probs[x,unlist(pred)[x]]
})
predVals = unlist(predVals)
pred= names(unlist(pred))
obsv = as.character(testingRaw$class)
pred[which(pred %in% c("FIBRE","FUR","WOOD"))] = "OTHER"
obsv[which(obsv %in% c("FIBRE","FUR","WOOD"))] = "OTHER"
obsv = as.factor(obsv)
pred = as.factor(pred)
cfMat = caret::confusionMatrix(pred,obsv)Note, that the classes which are not synthetic polymers are combined to a class called OTHER since we are only interested in the correct classification of plastic polymers. When a particle is correctly identified as non-plastic the main goal of the analysis is achieved, no matter if the different models disagree on the exact non-plastic class. By using the caret::confusionMatrix() function overall accuracy values are easily extracted as well as class specific metrics.
By calculating the average across all folds and all repeats final accuracy results are obtained.
| value | |
|---|---|
| Accuracy | 0.914 |
| Kappa | 0.894 |
| AccuracyLower | 0.824 |
| AccuracyUpper | 0.966 |
| AccuracyNull | 0.371 |
We achieved an overall accuracy of 91.4% with a Kappa coefficient of 0.89 (Tab. 1). This accuracy is substantially higher than compared to the single model accuracies of RF and CNN. By the decision fusion and combining the non-synthetic classes we were able to rise the accuracy by about 5%.
| Sensitivity | Specificity | Pos Pred Value | Neg Pred Value | Precision | Recall | F1 | Prevalence | Detection Rate | Detection Prevalence | Balanced Accuracy | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class: HDPE | 0.860 | 0.994 | 0.932 | 0.989 | 0.932 | 0.860 | 0.896 | 0.071 | 0.061 | 0.067 | 0.927 |
| Class: LDPE | 0.960 | 0.990 | 0.895 | 0.997 | 0.895 | 0.960 | 0.919 | 0.071 | 0.069 | 0.078 | 0.975 |
| Class: OTHER | 0.965 | 0.978 | 0.964 | 0.980 | 0.964 | 0.965 | 0.964 | 0.371 | 0.359 | 0.373 | 0.972 |
| Class: PA | 0.946 | 0.991 | 0.928 | 0.994 | 0.928 | 0.946 | 0.934 | 0.100 | 0.095 | 0.103 | 0.968 |
| Class: PE | 0.895 | 0.996 | 0.948 | 0.994 | 0.948 | 0.895 | 0.912 | 0.057 | 0.051 | 0.055 | 0.946 |
| Class: PES | 0.691 | 0.980 | 0.832 | 0.967 | 0.832 | 0.691 | 0.734 | 0.100 | 0.069 | 0.087 | 0.836 |
| Class: PET | 0.755 | 0.974 | 0.658 | 0.985 | 0.658 | 0.755 | 0.688 | 0.057 | 0.043 | 0.067 | 0.865 |
| Class: PP | 1.000 | 0.999 | 0.989 | 1.000 | 0.989 | 1.000 | 0.994 | 0.086 | 0.086 | 0.087 | 0.999 |
| Class: PS | 0.987 | 0.999 | 0.980 | 0.999 | 0.980 | 0.987 | 0.981 | 0.043 | 0.042 | 0.043 | 0.993 |
| Class: PUR | 0.920 | 0.999 | 0.978 | 0.997 | 0.978 | 0.920 | 0.960 | 0.043 | 0.039 | 0.041 | 0.959 |
Analyzing the class-specific accuracy metrics (Tab. 2) we observe that the lowest sensitivity is 0.69 for PES. That means that PES is most likely not to be identified correctly. PET shows a similar low sensitivity value of 0.76. PP shows the highest sensitivity value of 1, which means that all samples classified as PP actually represent that class. Concerning the specificity, gor all classes similar values of about 0.99 are observed. In general, it can stated that a very good distinction between non-synthetic polymers and microplastic polymers was achieved. However, some microplastic classes, such as PES and PET achieve only poor accuracies (0.84 and 0.86 of balanced accuracy respectively). These shortcomings might be compensated for with extensions to the reference database. Machine learning algorithms can only learn from the database which is presented to them. Therefore, adding reference samples could prove beneficial when it comes to accuracy.
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 highr_0.8 Rcpp_1.0.2 scales_1.0.0
[29] backports_1.1.4 jsonlite_1.6 fs_1.3.1 digest_0.6.20
[33] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2 tools_3.6.1
[37] lazyeval_0.2.2 tibble_2.1.3 crayon_1.3.4 whisker_0.3-2
[41] pkgconfig_2.0.2 zeallot_0.1.0 Matrix_1.2-17 assertthat_0.2.1
[45] rmarkdown_1.14 iterators_1.0.12 R6_2.4.0 git2r_0.26.1
[49] compiler_3.6.1