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It is shown that a better performance is obtained by fusing the classifiers.
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Since the resulting classifiers are not statistically independent, we propose a new method for fusing the classifiers.
In the next step, we investigated the effect of fusing the classifiers employing the different similarity measures.
The feature fusion was achieved by concatenating the features vectors extracted from the EEG and the HRV signals while the classifier fusion was accomplished by fusing the independent decisions from individual classifiers based on EEG and HRV.
Compared with the classic steel surface inspection system, the fuser Bayes kernel makes the key contribution to this system by fusing the results from the multiple classifiers and adjusting the hybrid parameters.
Although, the PCA-based classifiers perform nearly 3 times worse than the LDA-based one, an interesting finding of this paper compared to our previous work [19] is that the performance of the verification system can be further improved by fusing the LDA- and PCA-based classifiers.
(e) Can the performance be improved by fusing the decisions of all or a selected subset of the entire classifier ensemble?
Finally, it fuses the classifiers using the majority vote method.
The SVM classifier was used for fusing the selected classifiers.
Nevertheless, since Plant-mPLoc is an ensemble classifier formed by fusing many basic individual classifiers as well as by incorporating functional domain and evolution informations, it would be relatively more competent in dealing with the cases of mutated sequences than those predictors based on single classifier alone.
Then, the final result is achieved by fusing results from the combined classifiers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com