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In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and decision tree classifier as decision making.
In the current approach, however, the extracted HRV and EEG features were submitted to a two-phase wrapper-based feature selection process described in [48].
Classifier development : An L2/ridge-penalized (with the penalty parameter fixed at 2.5, based on optimization of cross-validated model likelihood in previous unrelated projects) logistic regression modeling strategy was employed for feature selection using markers selected by a penalized linear regression forward stepwise feature selection process.
In "Mutual information based feature selection and sampling rate adjustment", a mutual information based feature selection and sampling rate adjustment scheme have been proposed.
To improve performance of the predictor of drug-target interaction, feature selection process was carried out.
Support vector based feature selections are superior to LOGICFS, and LOGICFS is better than parametric and non-parametric based feature selections.
The feature selection process based on DET is briefly introduced.
The framework performs a two-step feature selection process based on both information theoretic criteria and regularization concept.
With the aim to ease the feature selection process, we selected only those genes with prognostic value based on two external databases, (Prognoscan and GeneSigDB) and significant correlation between CNA and gene expression.
In addition, a fine-tuning mechanism to update the population (based on mutual information) is designed and embedded in the feature selection process to speed up convergence.
Each file is then parsed and a vector representing each file is extracted based on a pre-determined vocabulary (which can be an outcome of setup feature selection process).
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