Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications
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Date
2024
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Abstract
Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been efectively
and efciently applied for feature selection to handle the uncertainty in real-valued datasets. Further,
Fuzzy rough mutual information has been presented by integrating information entropy with
fuzzy rough set to measure the importance of features. However, none of the methods till date can
handle noise, uncertainty and vagueness simultaneously due to both judgement and identifcation,
which lead to degrade the overall performances of the learning algorithms with the increment in
the number of mixed valued conditional features. In the current study, these issues are tackled by
presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular
structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular
structure is introduced. Then, IF rough conditional and joint entropies are established. Further,
mutual information based on these concepts are discussed. Next, mathematical theorems are proved
to demonstrate the validity of the given notions. Thereafter, signifcance of the features subset is
computed by using this mutual information, and corresponding feature selection is suggested to
delete the irrelevant and redundant features. The current approach efectively handles noise and
subsequent uncertainty in both nominal and mixed data (including both nominal and category
variables). Moreover, comprehensive experimental performances are evaluated on real-valued
benchmark datasets to demonstrate the practical validation and efectiveness of the addressed
technique. Finally, an application of the proposed method is exhibited to improve the prediction of
phospholipidosis positive molecules. RF(h2o) produces the most efective results till date based on our
proposed methodology with sensitivity, accuracy, specifcity, MCC, and AUC of 86.7%, 90.1%, 93.0% ,
0.808, and 0.922 respectively.