Covering assisted intuitionistic fuzzy bi‑selection technique for data reduction and its applications
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Date
2024
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Abstract
The dimension and size of data is growing rapidly with the extensive applications of computer science
and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy,
irrelevancy, and noise, which imposes concerns in building efective learning models. Fuzzy rough set
and its extensions have been applied to deal with these issues by various data reduction approaches.
However, construction of a model that can cope with all these issues simultaneously is always a
challenging task. None of the studies till date has addressed all these issues simultaneously. This paper
investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these
obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish
this task, frstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set
model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the
established similarity relation and the lower approximation. Next, the mathematical theorems are
used to validate the proposed notions. Then, the importance-degree of the IF granules is employed
for redundant size elimination. Further, signifcance-degree-preserved dimensionality reduction is
discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional
datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size,
where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise
is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over
the benchmark datasets to demonstrate the efectiveness of simultaneous feature and data point
selection methods. Finally, our proposed methodology aided framework is discussed to enhance the
regression performance for IC50 of Antiviral Peptides.