Skip to main content

Table 5 Mean cross validation accuracies for different machine learning algorithms on the dataset [2]

From: A spatio temporal spectral framework for plant stress phenotyping

Method Cross validated training accuracy Test set accuracy
  Water Nitrogen Weeds Water Nitrogen Weeds
Decision trees [49] 93.39 63.66 60.36 86.90 47.62 65.48
\(\hbox {LDA}^{\mathrm{a}}\) [50] 96.10 68.47 75.68 94.05 78.57 75.00
\({{SVM}}^{\mathrm{b}}\) [51] 93.09 75.68 83.18 95.24 80.95 77.38
\(\hbox {KNN}^{\mathrm{c}}\) [52] 92.79 62.16 69.97 92.86 55.95 65.48
BaggedTrees [53] 94.89 67.57 71.47 91.67 63.10 69.05
Subspace discriminant [54] 94.59 70.57 75.08 94.05 75.00 72.62
Subspace KNN [54] 93.39 60.66 64.26 97.62 66.67 72.62
\(\hbox {RUSBoostedTrees}^{\mathrm{d}}\) [55] 95.20 69.37 69.37 97.62 63.10 71.43
  1. For detailed descriptions of the machine learning methods evaluated we refer the reader to the cited papers. The SVM classifier showed the best overall performance from the tested methods, on both the training and test data, indicating good generalization to novel inputs. The implementations for the classification methods provided by the MATLAB® Statistics and Machine Learning Toolbox were used. The specific parameters for each of the classifiers can be found within the MATLAB® functions provided in the accompanying software suite
  2. \({}^{\mathrm{a}}\) Linear discriminant analysis
  3. \({}^{\mathrm{b}}\) Support vector machine
  4. \({}^{\mathrm{c}}\) \(k-\)nearest neighbor
  5. \({}^{\mathrm{d}}\) Randomly undersampled boosted trees