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KNN is an older classifier whose low computational cost is sometimes more important than the high accuracy for large-scale problems. In this study, we chose the Euclidean distance as the dissimilarity measure to construct the training set. The nearest neighbor plays an important role because the available training set was collected from dissimilar samples of *P. rosae* and *P. xylostella*. So there is a large difference between the training samples and the testing sample. To test the accuracy of KNN, we use the geometrically closest 10 neighbors as the training samples and the testing sample respectively. The accuracy rate of KNN is calculated as the average of the accuracy of ten trials.

Sample KNN
—————————————— ——–
**Single Feature**
Min. 0.7063
Max. 0.9583
Average (over 10 trials) 0.7518
**Two Feature**
Min.

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