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Predictability and interpretability are two competing goals and usually compromise each other for predictive machines.
In the case of only using GCC in Eq. (8) when k=0, the corresponding feature selection takes predictability and interpretability into account, as shown in Fig. 1d.
For example, in the case of heterologous gene expression modules, certain arrangements of the selection marker and the gene expression cassette may have unintended consequences that limit the predictability and interpretability of module behaviors.
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Fig. 1 Feature selection criteria based on predictability, stability, and interpretability.
Section 3 illustrates the proposed methodology and other feature selection methods using different criteria based on predictability, stability and interpretability.
In this section, extensive experiments are conducted to illustrate the effectiveness of SIP-FS in terms of predictability, stability, and interpretability.
This section presents feature selections and their corresponding results using different criteria based on predictability, stability, and interpretability, as shown in Fig. 1.
If k≠0 in Eq. (8), it means that the feature selection simultaneously takes predictability, stability, and interpretability into account, which is the so-called SIP-FS method in this paper, as shown in Fig. 1e.
Similar to predictability and stability, interpretability is essential for feature selection [20].
The validity of the models was assessed by Q (predictability) and R (interpretability) of the model.
To obtain compact data representation, a novel feature selection method is proposed to improve stability, and interpretability without sacrificing predictability (SIP-FS).
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