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J48 decision tree (DT) is used for both feature selection and classification.
We discuss our transformation model and validate both feature selection and model, based on a selection of the Emo-DB corpus.
Here we present what happens when one judiciously combines both feature selection and penalising out absence of features.
Additionally, both feature selection and extraction are applied on the combined feature set (SFS + S-PCA, FMS + S-PCA).
In [16], a combination of both feature selection and generation was proposed based on a modified GA.
In both, feature selection and parameters selection, we choose an optimal set in order to maximize the precision of the algorithm.
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Vemuri et al. [37] also used SVM as both feature selection criteria and a classifier, and achieved a sensitivity of 86 % and a specificity of 92 % in AD diagnosis on MRI data.
Both feature selection techniques and the proposed method can simultaneously be applied to increase the classification accuracy.
Additional file 10: Including mathematical detail of both feature selection (MEMBAS) and classification (LAMDA) algorithms.
Further comparison of the results of both feature selection methods SANN/LDA and GA/MLHD revealed identical selected genes (17/17) including all candidates (11/11) as well as the validated candidate genes (6/6) (Table 1).
Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.
More suggestions(15)
both subject selection and
both feature creation and
both sentence selection and
both stock selection and
both feature mapping and
both feature relevance and
both feature correspondence and
both size selection and
both balancing selection and
both feature detection and
both feature congestion and
both feature size and
both flowchart selection and
both route selection and
both feature compensation and
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