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The classification successes of feature combinations which are used in classification are compared.
Accuracy is estimated as an average of classification successes calculated by including the sample omitted in the LOOCV round, and by evaluating its label (control or pain) by means of the SVM separation line.
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In all cases, the method presents a 100% classification success.
A decision tree model with 97.5% classification success was developed based on SO42 − and Cl− variables.
Additionally, factors limiting the classification success shall be identified and addressed.
But, this technique has got limited classification success with fuzzy clustering algorithms as classifiers.
The superiority of the GA-ANN method was manifested in training accuracy and classification success rate.
The overall classification success rate for the entire data set is 98%.
It showed a higher classification success but performances were still low for more problematic taxa.
The Random forest algorithm performed better with 68% classification success compared to the taxonomic distance based results with 47% success for the total number of validation profiles.
Finally, we used an independent test set on which we evaluated our approach's classification success rate.
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