Last updated: 2019-08-19
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Knit directory: polymeRID/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d960dc2 | goergen95 | 2019-08-19 | included calibration |
For the calibration we implemented a decision fusion between the best performing models in the exploration stage. Since SVM-based models did not achieve very high accuracies we only included two RF models and two CNN. The RF models yielding to the highest accuracies were trained with with the raw data and the Savitzkiy-Golay smoothed data. For the CNNs, we observed the highest accuracies with with the second derivative of the raw data and with the second derivative of the normalized data. These models are going to be used during calibration. To get an accuracy value for the fusion approach, again we use and cross-validation approach.
The cross-validation of the decision fusion was conducted on 10 folds and repeated 5 times. The complete code can be found here. For every fold, 4 models are trained and evaluated against a 50% test split. The final 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 we combine the classes which are not synthetic polymers to a class called OTHER since we are only interested in the correct classification of plastic polymers. If a particle is correctly identified as non-plastic the main goal of the analysis is achieved, no matter if the different models disagree on exact non-plastic class. By using the caret::confusionMatrix() function we easily get overall accuracy values as well as class specific metrics.
By calculating the average across all folds and all repeats we end up with our final accuracy results per class and in general.
| 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 is 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 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 |
When we analyse 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 to not 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, across all classes we observe similar values of about 0.99. In general, we can state that we achieved a very good distinction between non-synthetic polymers and microplastic polymers. However, some microplastic polymers, 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 future extensions of the training data base which could include more samples, especially for these two classes as well as others. In the end, the machine learning algorithms can only learn from the data base 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