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The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches.
This multi classification method depends on basic voting principle.
The idea lays behind multi classification is just using more than one SVM and classifing the data according to the outputs of multiple SVMs.
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In this paper, the one-against-one method is used to construct the multi-classifier to solve the multi-classification problem.
Finally, the support vector machine multi-classification algorithm was employed for load recognition.
This paper introduces a system for the multi-classification of PES schemes.
However, it is known that most of LAD-based multi-classification algorithms have conflicts between classification accuracy and computational complexity because they are based on class decomposition method such as one versus all or one versus one.
multi-classification crime.
FSVM was also used to solve multi-classification problems [79].
Therefore, in this paper, OAO approach was used for each binary classifier to train the multi-classification model.
Results show that a support vector machine achieves the best results with about 60% accuracy on the multi-classification problem.
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