Based on MRMR method, Incremental Feature Selection (IFS) has been employed to determine how many features in the list MRMR generated should be selected [ 5].
d Selecting a characterizing subset of features based on mRMR for classification.
Because each sample was originally represented by a 35-dimensional feature vector based on the mRMR ordered feature list, 35 candidate feature sets were built.
Based on the sorted feature in mRMR feature list, the 270 feature subsets were constructed according to Eq. (14).
Stage 2. Based on the mRMR evaluation, incremental feature selection procedure [ 31, 32] is adopted to search for the optimal feature subset with the help of kernel sparse representation classification (KSRC) algorithm.
In the IFS procedure, we built 500 feature sets based on the ordered feature set S obtained in the mRMR step.
Based on the features ranked by mRMR, we used incremental feature selection (IFS) [ 21, 28, 39, 40] to determine the optimal number of features.
Incremental Feature Selection (IFS), an effective feature selection method based on the mRMR method.
With the feature selection approach based on the mRMR method and IFS procedure, we found that the following seven features would play the major roles in determining the stability of proteins: KEGG enrichment scores, subcellular locations, polarity, amino acids composition, hydrophobicity, secondary structure propensity, and the number of protein complexes.
Then we implemented the IFS procedure based on the mRMR rank list and generated 1900 feature sets.
