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On the other hand, it is possible to use a feature selection algorithm to remove the redundant features and to improve the overall accuracy of the problem.
One of the possible ways to rank the genes is to use a feature selection (FS) method.
To overcome this problem, we use a feature selection approach based on LASSO regression (Bach, 2008; Haury et al., 2012; Meinshausen and Bühlmann, 2010; Tibshirani, 1996).
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Our approach also differs in the way that AFs are evaluated (individually in our case), and in how AF populations are successively selected using a feature selection algorithm based on the syntactic structure of AFs, as described in the next section.
The proposed solution, referred to as the heterogeneous defect prediction (HDP) approach, is to first select the important features from the source domain using a feature selection method to eliminate redundant and irrelevant features.
This allows us to separate out the biologically relevant variability due to physical motion using a feature selection technique, Lasso [ 16].
The system was optimized by eliminating unimportant features using a feature selection method (UTA method).
To increase the generalizability of the models and accuracy of the predictions, we used a feature selection method to reduce the number of variables used in each model.
First, a subset of available features is identified using a feature selection algorithm.
Furthermore, the authors used a feature selection algorithm, leading to an increase of the predictive performance.
Our method uses a feature selection algorithm which improved classification accuracy by excluding redundant and non-discriminative features.
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