The effect of mis-labeled training data on the accuracy of supervised image classification by SVM

The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular, the SVM, which has been claimed to be relatively insensitive to training data error, showed the greatest sensitivity with overall accuracy declining by 8% with the use of a training set containing 20% mis-labeled cases; the difference in accuracy from that obtained without mis-labeled cases was statistically significant at the 95% level of confidence. Training data quality needs consideration when undertaking a supervised classification and should be considered in the selection of a classifier as the effects will be classifier-specific.